Starkey Research & Clinical Blog

Can hearing aid settings improve working memory?

Souza, P., & Sirow, L. (2014). Relating working memory to compression parameters in clinically-fit hearing aids. American Journal of Audiology, Just Accepted, released August 14.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Working memory provides short-term processing and storage of information during complex cognitive tasks, combining information from numerous sources into a coherent whole (Baddeley, 1992). Incoming stimuli are compared and matched to long-term memory representations, prior to identification and further processing.

The term working memory describes our ability to store and process information during cognitively demanding tasks. In the context of hearing, working memory capacity affects ones ability to match speech inputs with stored representations of that speech. Several studies suggest that individuals with impaired working memory experience increased difficulty understanding speech in complex listening environments (Lunner, 2003). It is assumed that working memory tends to decline with advancing age (Salthouse, 1994). Therefore, it is important to understand how these variables affect speech perception and how they interact with each other, particularly for older hearing aid users.

Working memory may impact the optimal hearing aid characteristics for an individual and a number of studies have investigated the relationship between working memory and wide-dynamic range compression (Foo, et al., 2007; Gatehouse, et al., 2006; Lunner & Sundewall-Thoren, 2007; Ohlenforst, et al., 2014).  In these studies,  hearing aid compression speed was examined while keeping other amplification characteristics constant. Subjects with better working memory were generally found to perform better with fast-acting compression, whereas subjects with poorer working memory performed better with slow-acting compression.  The authors interpret these results as an indication that fast-acting compression alters the speech envelope in ways that make it more difficult to match incoming stimuli to stored lexical representations (Jenstad & Souza, 2007; Jenstad & Souza, 2005; Ronnberg et al., 2013; Ronnberg et al., 2008).

Laboratory experiments inherently must control the variables under study in order to glean meaningful interpretations. However, comparing fast and slow compression speed in isolation does not represent the typical conditions of a clinical hearing aid fitting, in which these characteristics are not independently adjustable. Furthermore, with changes in compression speed from one hearing aid model to another, many other variables are likely to differ as well, such as feedback management, noise reduction characteristics and the number of compression channels. These considerations make it difficult to extrapolate laboratory findings to everyday clinical experiences. The goal of Souza and Sirow’s study was to examine how compression speed and working memory relate to each other, using selection, fitting and verification techniques as they would typically be used in a clinical setting.

Twenty-seven participants with hearing loss were fitted with at least three different sets of receiver-in-canal hearing instruments, from several manufacturers. Because only one manufacturer offered an aid with adjustable compression speed, each subject completed a comparison of two compression settings with this single hearing aid, plus two or three additional models from other manufacturers that varied in their compression characteristics.  All aids were fitted with closed domes in the appropriate size for the individual. Real-ear verification and adjustments to prescribed levels were completed as they would in a typical clinical hearing aid fitting, to ensure audibility and comfort.  Aids were programmed with omnidirectional microphones and special hearing aid parameters such as feedback management and noise reduction were set according to manufacturer defaults.

Working memory is often assessed with a dual-paradigm task, in which the subject is required to process information while storing it for later recall. In this study, working memory was assessed with a reading span test, the same procedure used in previous studies of hearing aid compression and working memory. Subjects were presented with five-word sentences flashed on a computer screen and were asked to judge if the sentences made sense or not.  Sentences were presented one at a time in blocks of three, four or five. After each block, subjects were asked to recall either the first or last words of the sentences. The working memory score was taken as the percentage of correctly recalled words across all blocks.

Speech intelligibility was tested using the QuickSIN (Killion, et al., 2004) test, because of its ease of clinical administration and similarity to test materials and conditions in prior studies of compression (Lunner & Sundelwall-Thoren, 2007).  The test was administered in a sound booth via loudspeaker at a 0-degree azimuth, at 70dBHL for most subjects.  The QuickSIN yields an SNR loss score, which indicates the increase in SNR required to achieve a performance threshold. Larger SNR loss scores represent poorer performance.

Correlations were calculated to examine the relations among working memory, age, degree of hearing loss and speech-in-noise performance.  Not surprisingly, increases in age and degree of hearing loss were associated with poorer scores on the QuickSIN test. Working memory scores were also significantly correlated with aided QuickSIN scores.  Lower working memory scores were loosely associated with increased age and poorer unaided QuickSIN scores, but these relationships did not reach significance.

Reading span test scores, representing working memory, ranged from 17% to 50%, with a mean of 34%.  As in previous studies, subjects were divided into high and low working memory groups, based on the median score for the group. For slower compression speeds, comparable performance was achieved by both high and low working memory groups. At faster compression speeds, individuals in the high working memory group performed better than those in the low working memory group. For the fastest compression times, the difference in SNR loss between the high and low working memory groups was greater than 5dB. The authors point out that this is a substantial difference, as a QuickSIN SNR loss difference of 2.7dB is considered significant.

Aided QuickSIN scores were significantly affected by working memory for fast compression speed, but not for slow compression speed. There was high variability in the scores, especially for slow compression times, so further analysis was conducted to examine the contributions of other variables. For fast compression speeds, working memory and hearing loss accounted for most of the variance. For slow compression speeds, age and hearing loss were significant predictors of performance, but working memory was not.

The results of this study are consistent with previous reports suggesting that listeners with low working memory may not perform well with fast acting compression, whereas those with high working memory can be expected to do better.  The findings of the current study appear particularly robust because they emerged under less controlled conditions than in the laboratory studies. The authors point out that even in the hearing aid that allowed manipulation of compression speed, changing it resulted in other changes in signal processing as well.  The fact that compression speed still had a significant effect on speech-in-noise performance under these conditions is support for its relationship with working memory.

Though further study is needed to illuminate the relationship between working memory and the selection of hearing aid parameters, there are a number of potential benefits to incorporating working memory tests into clinical practice. The working memory assessment could help to explain poor performance with a current set of hearing aids and indicate the need for new aids or adjustments to signal processing parameters, if possible. Souza and Sirow offer a cautionary statement regarding the use of working memory assessment during a diagnostic hearing evaluation. They suggest that patients may not understand the link between auditory assessment and a task that could involve assessment of memory. With that cautionary consideration, hearing care providers may be more likely than other clinical professionals to recognize symptoms of cognitive decline. Atypical results of a working memory assessment may provide insight into a patient’s performance with hearing aids as well as their general cognitive health, prompting referrals to a primary care physician or other specialists.

The study of hearing loss, hearing aids, cognition and memory is an interesting area of inquiry with potentially important implications for clinical hearing aid fitting. Souza and Sirow’s report on the relationship between working memory and compression speed illustrates how individual variability in working memory could have specific impact on the selection of hearing aid characteristics.  Their findings represent an important link between laboratory investigation on this topic and the clinical prescription of hearing aids.


Baddeley, A. (1992). Working memory. Science 255 (5044), 556-559.

Foo, C., Rudner, M., Ronnberg, J. & Lunner, T. (2007). Recognition of speech in noise with new hearing instrument compression release settings requires explicit cognitive storage and processing capacity. Journal of the American Academy of Audiology 18(7), 618-631.

Gatehouse, S., Naylor, G. & Elberling, C. (2006). Linear and nonlinear hearing aid fittings: 2. Patterns of candidature. International Journal of Audiology 45(3), 153-171.

Jenstad, L. & Souza, P. (2005). Quantifying the effect of compression hearing aid release time on speech acoustics and intelligibility. Journal of Speech, Language and Hearing Research 48(3), 651-667.

Jenstad, L. & Souza, P. (2007). Temporal envelope changes of compression and speech rate: combined effects on recognition for older adults. Journal of Speech, Language and Hearing Research 50(5), 1123-1138.

Killion, M., Niquette, P., Gudmundsen, G., Revit, L. & Banerjee, S. (2004). Development of a quick speech-in-noise test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired listeners. Journal of the Acoustical society of America 116(4), 2395-2405.

Lunner, T. (2003). Cognitive function in relation to hearing aid use. International Journal of Audiology 42 (Suppl.): S49-S58.

Lunner, T. & Sundewall-Thoren, E. (2007). Interactions between cognition, compression and listening conditions: effects on speech-in-noise performance in a two-channel hearing aid. Journal of the American Academy of Audiology 18(7), 604-617.

Ohlenforst, B., Souza, P. & MacDonald, E. (2014). Interaction of working memory, compressor speed and background noise characteristics. Paper presented at the American Auditory Society, Scottsdale, AZ.

Remensnyder, L. (2012). Audiologists as gatekeepers and it’s not just for hearing loss. Audiology Today, July/August,  24-31.

Ronnberg, J., Rudner, M., Foo, C. & Lunner, T. (2008 ). Cognition counts: a working memory system for ease of language understanding (ELU). International Journal of Audiology 47(Suppl. 2), S99-105.

Ronnberg, J., Lunner, T., Zekveld, A., Sorqvist, P., Danielsson, H., Lyxell, B. & Rudner, M. (2013). The Ease of Language Understanding (ELU) model: theoretical, empirical and clinical advances. Frontiers in Systems Neuroscience 7, 31.

Rudner, M., Foo, C., Ronnberg, J. & Lunner, T. (2009). Cognition and aided speech recognition in noise: specific role for cognitive factors following nine-week experience with adjusted compression settings in hearing aids. Scandinavian Journal of Psychology 50(5), 405-418.

Salthouse, T. (1994). The aging of working memory. Neuropsychology 8(4), 535-543.

Considerations for Music Listening

Croghan, N., Arehart, K. & Kates, J.  (2014). Music preferences with hearing aids: effects of signal properties, compression settings and listener characteristics. Ear & Hearing, in press.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

For the hearing aid wearer, speech is arguably the most important sound but hearing aid satisfaction is affected by the way in which the devices process other environmental sounds, including music. Adoption of hearing aids by active, technology savvy users makes their ability to process music with optimal sound quality and minimal distortion is more important than ever.  Though modern hearing aids do an effective job of processing speech, even in the presence of competing noise, many hearing aid users report that hearing aids either make no difference or make music less enjoyable (Leek et al., 2008).

Music and speech have different spectral and temporal characteristics, with music often being higher in intensity and more dynamic than speech (Chasin, 2003, 2006, 2010).  Speech maintains somewhat similar and predictable acoustic characteristics across talkers; in contrast, the spectral and temporal characteristics of music vary widely from one instrument to another and one piece to another (Chason & Russo, 2004). Not surprisingly, some studies have indicated that the best hearing aid circuit characteristics and settings for speech recognition may not be optimal for music perception (Higgins et al, 2012; van Buuren et al., 1999). For instance, faster compression time constants may be helpful for restoring speech audibility and loudness perception (Moore, 2008) but listeners may prefer longer release times for listening to music (Hansen, 2002; Moore, 2011).

Recorded music heard by hearing-aid users is subject to two stages of compression; compression limiting during the studio recording and wide-dynamic range compression in the hearing aid. Processing at both of these stages could impact music sound quality and subsequent enjoyment by the listener.  Croghan and colleagues investigated the acoustic and perceptual effects of compression on music processed through hearing aids. They examined the effect of compression limiting prior to hearing aid processing and compared slow versus fast hearing aid compression time constants as well as small versus large numbers of channels. In addition to these compression variables, they examined potential effects of suprathreshold processing and prior musical training.

Eighteen hearing aid users, ranging from 49 to 87 years of age, participated in the study. Subjects were divided into non-musician and musician groups. Two pieces of music, one classical and one rock, were used in the study. The pieces were selected to be relatively unfamiliar to the subjects, to reduce any effect of prior experience or expectations. To simulate studio processing, the music samples were recorded in three compression limiting conditions: no compression, mild compression limiting, and heavy compression limiting.  These compression conditions were applied to the music samples prior to hearing aid processing.

Music was presented over binaural headphones, via a simulated hearing aid.  Individual WDRC hearing aid simulations were programmed according to NAL-NL1 formulae for each subject. Stimuli were processed with two sets of compression release times and processing channels: fast (50msec) vs. slow (1000msec) release times and 3-channels vs. 18 channels.  Two linear conditions were also included, using the NAL-R prescription with 3-channels and 18-channels for frequency shaping.  The combination of 3 compression limiting, 4 WDRC and 2 linear conditions resulted in 18 processing conditions for each piece of music.   Stimuli were presented at 65dB SPL and subjects made preference judgments in a 2-interval forced-choice paradigm.

To examine the effect of suprathreshold processing, three psychophysical tests were administered. Loudness perception was measured with the Contour Test of Loudness Perception (Cox et al., 1997), amplitude modulation depth discrimination was measured using speech-shaped noise modulated at 4Hz and frequency selectivity was measured with psychophysical tuning curves (Sek et al. 2005; Sek & Moore, 2011). The music stimuli were also analyzed with a modification of the Hearing Aid Speech Quality Index (HASQI; Kates & Arehart, 2010). Roughly stated, the HASQI provides an object sound quality rating by comparing time-frequency modulation and long-term spectrum of an unmodified signal to a modified one (the modified signal being one with the targeted signal processing applied). Not surprisingly, the lowest HASQI values, indicating the most difference between unprocessed and processed stimuli, were observed for fast WDRC combined with heavy compression limiting.

The 18 stimulus conditions were examined for the effect of compression on the overall dynamic range, amplitude by frequency and modulation of the music samples.  Generally any increase in processing – increasing compression limiting, increasing the number of channels, going from linear to slow WDRC or slow to fast WDRC – reduced the dynamic range of the classical and rock music samples. WDRC caused more dynamic range reduction in the high frequencies. Compression limiting affected classical music similarly across frequencies, whereas rock music was affected more in the high and low-frequency regions than in the mid-frequencies. Compression – either WDRC or limiting – reduced the magnitude of modulation, likely making the rhythmic structure of the music less distinguishable.

Listener preference results for compression limiting and WDRC indicated some differences based on the type of music that was presented. For classical music, there was no significant difference between slow WDRC and linear processing, but both of these were preferred over fast WDRC. Mild or no compression was significantly preferred over heavy compression limiting.  There was no effect for the number of channels on classical music preferences.  Slightly different results were obtained for rock music: linear processing was preferred over both WDRC conditions and slow WDRC was significantly preferred over fast WDRC. There was no significant effect of compression limiting but the 3-channel condition was rated significantly better than the 18-channel condition.

The following listener-related factors were examined for their effects on preference: gender, musician vs. non-musician, PTA, dynamic range, tuning curve bandwidth and modulation depth discrimination threshold. Because PTA and dynamic range were strongly correlated to each other, these factors were excluded from the analysis.  For classical music, the only significant findings were interactions among tuning curve bandwidth, WDRC condition and number of processing channels. Listeners with broader tuning curves showed a slight preference for linear amplification over WDRC and 3-channel over 18-channel processing. In contrast, the group with narrower tuning curves had a slight preference for slow WDRC and 18-channel processing. There were no other significant findings for listener-related factors. The authors posit that listeners with better frequency resolution may have preferred slow WDRC and 18-channel processing because they were able to resolve the harmonics and benefit from greater audibility. Conversely, listeners with poorer frequency resolution may have responded to reduced distortion in the linear and 3-channel conditions, despite potentially reduced audibility.

In this study, Croghan and her colleagues found that for music stimuli, compression limiting and WDRC reduced temporal envelope contrasts. These results are in agreement with previous studies using speech stimuli (Bor et al., 2008; Jenstad & Souza, 2005). They also found that compression limiting was more likely than WDRC to reduce the peaks of the modulation spectrum. This is somewhat in agreement with a previous report by Souza & Gallun (2010) on consonant discrimination, in which hearing aid compression limiting had an adverse effect but multi-channel, fast WDRC was beneficial.  However, the authors point out that hearing aid compression limiting is different from music industry compression limiting in that the former compresses only high-level sounds and does not affect average (RMS) sound level.

The results of this study indicate that music was adversely affected by compression limiting and WDRC and that in general, listeners preferred listening to music with little or no compression. Listeners with broad psychophysical tuning curves showed a preference for 3-channel processing, whereas those with narrower tuning curves preferred 18-channel processing. This may be related to the ability of those with narrower tuning curves to perceive harmonics, especially in the classical piece, which is related to the perceived quality of stringed instruments (Chasin & Russo, 2004).  This result is similar to a report using speech stimuli by Souza et al. (2012) in which listeners with better frequency resolution were more able to benefit from multi-channel compression. More research is needed to illuminate the relationship between suprathreshold processing and music perception. Traditional measures of psychophysical tuning curves is an unwieldy proposition for clinicians, but hearing aid users with impaired frequency resolution may require a modified treatment approach.

In contrast to previous studies in which musicians outperformed non-musicians on tests of frequency discrimination, speech discrimination and working memory (Parbery-Clark et al., 2009; 2012), Croghan and her colleagues found no significant difference in the psychophysical tests or preference ratings for musicians versus non-musicians.  They point out, however, that their study used recorded music samples and preferences for live music cannot be extrapolated from their results. Clinicians should expect musicians to be analytical about the sound quality of their hearing aids and be prepared to offer a separate, manually accessible programs for music listening. Similarly, many non-musicians are music aficionados who would also appreciate an alternate program for music. In many hearing instruments, alternate music programs can be added using defaults available in manufacturer software, or can be individually customized. In music listening programs, special features like automatic directionality and noise reduction should be disabled and based on Croghan’s report, should probably have more linear processing and less compression than primary, everyday listening programs.

Croghan’s study provides insight into the ways in which hearing aid signal processing affects music acoustics and perception. Our current knowledge of music acoustics and hearing aid signal processing may be more meaningfully applied to the technical design of hearing aids than to routine clinical practice. While opportunities remain for meaningful advancement in the processing of music through hearing aids, some clinical advice can be offered:

  • Musicians or individuals with strong musical interests may benefit from a dedicated memory, optimized for music listening.
  • Optimization of a dedicated music listening memory is best attempted following a patient’s initial adaptation period to new hearing aids.
  • Follow-up visits addressing music perception and sound quality should include multiple music samples of the patient’s own selection.
  • Using default settings for music listening, the patient should be prompted to set the playback loudspeakers to a level they find pleasing for a given music sample.

Although the preferences of each patient are different, these suggestions are a solid foundation for providing patients with a high-quality music listening experience.



Chasin, M. (2003). Music and hearing aids. Hearing Journal 56, 36-41.

Chasin, M. (2006). Hearing aids for musicians. Hearing Review 13, 11-16.

Chasin, M. (2010). Amplification fit for music lovers. Hearing Journal 63, 27-30.

Chason, M. & Russo, F. (2004). Hearing aids and music. Trends in Amplification 8, 35-47.

Cox, R., Alexander, G. & Taylor, I. (1997). The contour test of loudness perception. Ear and Hearing 18, 388-400.

Croghan, N., Arehart, K. & Kates, J.  (2014). Music preferences with hearing aids:

effects of signal properties, compression settings and listener characteristics. Ear & Hearing, in press.

Hansen, M. (2002). Effects of multi-channel compression time constants on subjectively perceived sound quality and speech intelligibility. Ear and Hearing 23, 369-380.

Higgins, P., Searchfield, G. & Coad, G. (2012). A comparison between the first-fit settings of two multichannel digital signal-processing strategies: music quality ratings and speech-in-noise scores. American Journal of Audiology 21, 13-21.

Leek, M., Molis, M. & Kubli, L. (2008).  Enjoyment of music by elderly hearing-impaired listeners. Journal of the American Academy of Audiology 19, 519-526.

Moore, B. (2008) . The choice of compression speed in hearing aids: Theoretical and practical considerations and the role of individual differences. Trends in Amplification 12, 103-112.

Moore, B., Fullgrabe, C. & Stone, M. (2011).  Determination of preferred parameters for multichannel compression using individually fitted simulated hearing aids and paired comparisons. Ear and Hearing 32, 556-568.

Moore, B. & Glasberg, B. (1997) A model of loudness perception applied to cochlear hearing loss. Auditory Neuroscience 3, 289-311.

Neumann, A., Bakke, M. & Hellman, S. (1995a). Preferred listening levels for linear and slow-acting compression hearing aids. Ear and Hearing 16, 407-416.

Parbery-Clark, A., Skoe, E. & Lam, C. (2009). Musician enhancement for speech-in-noise. Ear and Hearing 30, 653-661.

Parbery-Clark, A., Tierney, A. & Strait, D. (2012). Musicians have fine-tuned neural distinction of speech syllables. Neuroscience 219, 111-119.

Sek, A., Alcantara, J. & Moore, B. ( 2005). Development of a fast method for determining psychophysical tuning curves. International Journal of Audiology 44, 408-420.

Sek, A. & Moore, B. (2011). Implementation of a fast method for measuring psychophysical tuning curves. International Journal of Audiology 50, 237-242.

van Buuren, R., Festen, J. & Houtgast, T. (1999).  Compression and expansion of the temporal envelope: Evaluation of speech intelligibility and sound quality. Journal of the Acoustical Society of America 105, 2903-2913.

A Pediatric Prescription for Listening in Noise

Crukley, J. & Scollie, S. (2012). Children’s speech recognition and loudness perception with the Desired Sensation Level v5 Quite and Noise prescriptions. American Journal of Audiology 21, 149-162.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Most hearing aid prescription formulas attempt to balance audibility of sound with perception of loudness, while keeping the amplified sound within a patient’s dynamic range (Dillon, 2001; Gagne et al., 1991a; Gagne et al., 1991b; Seewald et al., 1985). Use of a prescriptively appropriate hearing aid fitting is particularly important for children with hearing loss. For the needs of language development, they benefit from a higher proportion of audible sound and broader bandwidth than diagnostically similar older children and adults (Pittman & Stelmachowicz, 2000; Stelmachowicz et al., 2000; Stelmachowicz et al., 2001; Stelmachowicz et al., 2004; Stelmachowicz et al., 2007).

Historically, provision of access to speech in quiet has been a primary driver in the development of prescription formulas for hearing aid.  However, difficulty understanding speech in noise is one of the primary complaints of all hearing aid users, including children. In a series of studies compared NAL-NL1 and DSL v4.1 fittings and examined children’s listening needs and preferences (Ching et al., 2010; Ching et al., 2010; Scollie et al., 2010) two distinct listening categories were identified: loud, noisy and reverberant environments and quiet or low-level listening situations. The investigators found that children preferred the DSL fitting in quiet conditions but preferred the NAL fitting for louder sounds and when listening in noisy environments. Examination of the electroacoustic differences between the two fittings showed that the DSL fittings provided more gain overall and approximately 10dB more low-frequency gain than the NAL-NL1 fittings.

To address the concerns of listening in noisy and reverberant conditions, DSL v5 includes separate prescriptions for quiet and noise. Relative to the formula for quiet conditions, the noise formula prescribes higher compression thresholds, lower overall gain, lower low-frequency gain and more relative gain in the high frequencies.  This study of Crukley and Scollie addressed whether the use of the DSL v5 Quiet and Noise formulae resulted in differences in consonant recognition in quiet, sentence recognition in noise and different loudness ratings.  Because of the lower gain in the noise formula, it was expected to reduce loudness ratings and consonant recognition scores in quiet because of potentially reduced audibility. There was no expected difference for speech recognition in noise, as the noise floor was considered the primary limitation to audibility in noisy conditions.

Eleven children participated in the study; five elementary school children with an average age of 8.85 years and six high school children with an average age of 15.18 years. All subjects were experienced, full-time hearing aid users with congenital, sensorineural hearing losses, ranging from moderate to profound.  All participants were fitted with behind-the-ear hearing aids programmed with two separate programs: one for DSL Quiet targets and one for DSL Noise targets. The Noise targets had, on average, 10dB lower low-frequency gain and 5dB lower high-frequency gain, relative to the Quiet targets. Testing took place in two classrooms: one at the elementary school and one at the high school.

Consonant recognition in quiet conditions was tested with the University of Western Ontario Distinctive Features Differences Test (UWO-DFD; Cheesman & Jamieson, 1996). Stimuli were presented at 50dB and 70dB SPL, by a male talker and a female talker. Sentence recognition in noise was performed with the Bamford-Kowal-Bench Speech in Noise Test (BKB-SIN; Niquette et al., 2003). BKB-SIN results are scored as the SNR at which 50% performance can be achieved (SNR-50).

Loudness testing was conducted with the Contour Test of Loudness Perception (Cox et al., 1997; Cox & Gray, 2001), using BKB sentences presented in ascending then descending steps of 4dB from 52dB to 80dB SPL. Subjects rated their perceived loudness on an 8-point scale ranging from “didn’t hear it” up to “uncomfortably loud” and indicated their response on a computer screen. Small children were assisted by a researcher, using a piece of paper with the loudness ratings, and then the researcher entered the response.

The hypotheses outlined above were generally supported by the results of the study. Consonant recognition scores in quiet were better at 70dB than 50dB for both prescriptions and there was no significant difference between the Quiet and Noise fittings. There was, however, a significant interaction between prescription and presentation level, showing that performance for the Quiet fittings was consistent at the two levels but was lower at 50dB than 70dB for the Noise fittings. The change in score from Quiet to Noise at 50dB was 4.2% on average, indicating that reduced audibility in the Noise fitting may have adversely affected scores at the lower presentation level. On the sentence recognition in noise test, BKB-SIN scores did not differ significantly between the Quiet and Noise prescriptions, with some subjects scoring better in the Quiet program, some scoring better in the Noise program and most not demonstrating any significant difference between the two. Loudness ratings were lower on average for the Noise prescription. When ratings for 52-68dB SPL and 72-80dB SPL were analyzed separately, there was no difference between the Quiet and Noise prescriptions for the lower levels but at 72dB and above, the Noise prescription yielded significantly lower loudness ratings.

Although the average consonant recognition scores for the Noise prescription were only slightly lower than those for the Quiet prescription, it may not be advisable to use the Noise prescription as the primary program for regular daily use, because of the risk of reduced audibility. This is especially true for pediatric hearing aid patients, for whom maximal audibility is essential for speech and language development. Rather, the Noise prescription is better used as an alternate program, to be accessed manually by the patient, teacher or caregiver, or via automatic classification algorithms within the hearing aid. Though the Noise prescription did not improve speech recognition in noise, it did not result in a decrement in performance and yielded lower loudness ratings, suggesting that in real world situations it would improve comfort in noise while still maintaining adequate speech intelligibility.

Many audiologists find that patients prefer a primary program set to a prescriptive formula (DSL v5, NAL-NL2 or proprietary targets) for daily use but appreciate a separate, manually accessible noise program with reduced low-frequency gain and increased noise reduction. This is true even for the majority of patients who have automatically switching primary programs, with built-in noise modes. Anecdotal remarks from adult patients using manually accessible noise programs agree with the findings of the present study, in that most people use them for comfort in noisy conditions and find that they are still able to enjoy conversation.

For the pediatric patient, prescription of environment specific memories should be done on a case-by-case basis. Patients functioning as teenagers might be capable of managing manual selection of a noise program in appropriate conditions. Those of a functionally younger age will require assistance from a supervising adult. Personalized, written instructions will assist adult caregivers to ensure that they understand which listening conditions may be uncomfortable and what actions should be taken to adjust the hearing aids. Most modern hearing aids feature some form of automatic environmental classification: ambient noise level estimation being one of the more robust classifications. Automatic classification and switching may be sufficient to address concerns of discomfort. However, the details of this behavior vary greatly among hearing aids. It is essential that the prescribing audiologist is aware of any automatic switching behavior and works to verify each of the accessible hearing aid memories.

Crukley and Scollie’s study supports the use of separate programs for everyday use and noisy conditions and indicates that children could benefit from this approach. The DSL Quiet and Noise prescriptive targets offer a consistent and verifiable method for this approach with children, while also providing potential guidelines for designing alternate noise programs for use by adults with hearing aids.



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Ching, T., Scollie, S., Dillon, H., Seewald, R., Britton, L. & Steinberg, J. (2010). Prescribed real-ear and achieved real life differences in children’s hearing aids adjusted according to the NAL-NL1 and the DSL v4.1 prescriptions. International Journal of Audiology 49 (Suppl. 1), S16-25.

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Niquette, P., Arcaroli, J., Revit, L., Parkinson, A., Staller, S., Skinner, M. & Killion, M. (2003). Development of the BKB-SIN test. Paper presented at the annual meeting of the American Auditory Society, Scottsdale, AZ.

Pittman, A. & Stelmachowicz, P. (2000). Perception of voiceless fricatives by normal hearing and hearing-impaired children and adults. Journal of Speech, Language and Hearing Research 43, 1389-1401.

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Scollie, S., Ching, T., Seewald, R., Dillon, H., Britton, L., Steinberg, J. & King, K. (2010). Children’s speech perception and loudness ratings when fitted with hearing aids using the DSL v4.1 and NAL-NL1 prescriptions. International Journal of Audiology 49 (Suppl. 1), S26-S34.

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Stelmachowicz, P., Hoover, B., Lewis, D., Kortekaas, R. & Pittman, A. (2000). The relation between stimulus context, speech audibility and perception for normal hearing and hearing impaired children. Journal of Speech, Language and Hearing Research 43, 902-914.

Stelmachowicz, P., Pittman, A., Hoover, B. & Lewis, D. (2001). Effect of stimulus bandwidth on the perception of /s/ in normal and hearing impaired children and adults. The Journal of the Acoustical Society of America 110, 2183-2190.

Stelmachowicz, P. Pittman, A., Hoover, B. & Lewis, D. (2004). Novel word learning in children with normal hearing and hearing loss. Ear and Hearing 25, 47-56.

Stelmachowicz, P. Pittman, A., Hoover, B., Lewis, D. & Moeller, M. (2004). The importance of high-frequency audibility in the speech and language development of children with hearing loss. Archives of Otolaryngology, Head and Neck Surgery 130, 556-562.

Stelmachowicz, P., Lewis, D., Choi, S. & Hoover, B. (2007).  Effect of stimulus bandwidth on auditory skills in normal hearing and hearing impaired children.  Ear and Hearing 28, 483-494.

On the prevalence of hearing loss and barriers to hearing aid uptake

Dawes, P., Fortnum, H., Moore, D., Emsley, R., Norman, P., Cruickshanks, K., Davis, A., Edmondson-Jones, M., McCormack, A., Lutman, M. & Munro, K.  (2014) Hearing in middle age: a population snapshot of 40- to 69-year olds in the United Kingdom. Ear & Hearing 35 (3), 44-51.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

The Biobank is a national program in the United Kingdom, aimed at longitudinal investigation of the prevention, diagnosis and treatment of diseases and health conditions affecting middle-aged individuals. Since 2006, the Biobank has recruited over half a million participants, who complete test procedures, provide biomedical samples and detailed health information and have their health followed over time, periodically providing updated information. One of the health conditions assessed in the Biobank study is hearing loss and over 160,000 participants have completed questionnaires, audiometric assessment and speech-in-noise testing.

Dawes and his colleagues used Biobank data to examine the prevalence of hearing impairment among 164,700 middle-aged respondents in the U.K., “hearing impairment” was defined as reduced or poor performance on a speech recognition in noise test. They assessed how audiologic and demographic factors relate to hearing impairment and the use of hearing aids among individuals in this age group.

For the Biobank database, hearing loss was assessed via audiometric testing and questionnaires covering lifestyle, environment and medical history, including associated symptoms such as tinnitus. Speech recognition in noise was assessed via the Digit Triplet Test (DTT; Smits et al., 2004). The DTT is a large-scale screening tool that can be administered via the telephone and internet.  The test includes 15 sets of monosyllable digit triplets, presented at a comfortable listening level. Noise levels are varied adaptively to arrive at the SNR required for 50% recognition. Speech recognition results were analyzed in relation to several demographic variables: age, work and music related noise exposure socioeconomic status, ethnicity and gender. 

10.7 % of participants had hearing impairment, as measured by the DTT. Tinnitus was reported by 16.9% of the subjects, which is consistent with previous reports (Davis 1995).  The results show, not surprisingly, that the prevalence of hearing loss increases with increasing age, with an acceleration of prevalence beginning in the 55-59 year old age group. The increase in prevalence with increasing age is consistent with previously published reports for this age group (Plomp & Mimpen, 1979; Wilson & Strouse, 2002; Smits et al., 2006). Tinnitus showed a more consistent increase with increasing age, without a steeper increase for respondents in their 50’s.  Hearing aid use was only 2% for the entire sample and increased with age.  Only 21% of the participants with Poor DTT scores reported using hearing aids.  Those who did use hearing aids had significantly higher socioeconomic status than those without hearing aids.

Only 2.0% of the middle-aged individuals in this study reported hearing aid use. This is similar to an earlier report in which hearing aid use for 41-70 year olds was 2.8% (Davis, 1995). The persistently low proportion of hearing aid use contrasts with the fact that 9.4% of the respondents in the current study had average pure tone thresholds of at least 35 dBHL in the better ear. There are many potential explanations for the low proportion of hearing aid use among hearing impaired individuals. Cost is a commonly cited explanation, though cost is not likely to have influenced the present report, as hearing aids are provided free in the United Kingdom and the participants included in this report probably did not purchase their hearing aids privately. Insufficient value and uncomfortable fit have also been reported as explanations for low hearing aid use (McCormack & Fortnum, 2013). Other proposed barriers to hearing aid use are related to motivation, expectations and attitudes toward hearing aids, with self-recognition of hearing handicap being the most consistently related factor to hearing aid use (Vestergaard-Knudsen et al., 2010).

One mechanism for addressing the concern of hearing aid cost is through the unbundling of the hearing aid and services provided. Bundled pricing (the packaging of hearing aid and services into one price) is typical in the U.S. Unbundling may encourage initial uptake because it allows hearing aid users to pay less at the outset and divide additional expenditures into smaller, more manageable amounts, paying fees at each visit after the initial service period. There is some concern that unbundled pricing will make hearing aid users less likely to obtain needed care, but this fear may be overstated. Hearing aid users generally indicate that verification measures and counseling increase satisfaction and perceived value of hearing aids (Kochkin, 2010; 2011), so follow-up care can be perceived by the patient as a valuable part of the rehabilitative process. Unbundling offers the additional benefit for private practices because fee-for-service appointments lead to more consistent monthly cash flow than bundled fees in which a large initial payment is received with free services for a long time thereafter.

The manner in which hearing aids are represented to the general public may further impact uptake. Hearing aids are best positioned as medical devices, prescribed by skilled professionals, in clinical settings where testing is performed in controlled acoustic environments. If price is prioritized, then testing, verification and follow-up care may be abbreviated to control costs. If cosmetic appeal is prioritized, patients may select the smallest devices, perhaps without adequate venting or directional microphones, though this might not be the best option for their loss and listening needs. The potential outcome of both scenarios is disappointment with the performance and comfort of the hearing instruments, resulting in either lack of use or return for credit.  Instead, hearing aid users need to be fully educated about the options that are available and counseled as to why some models are better for their needs than others. This cannot be achieved in an environment that emphasizes price over functionality and service.

As Dawes points out, hearing impairment may be better defined by speech recognition ability in everyday situations, rather than pure tone audiometry. Even so, it is arguable whether either of these measures alone should be used to define hearing aid candidacy. Instead, clinicians gain more insight into their patients’ motivation and readiness by examining how the hearing loss affects their ability to function in their regular activities. A mildly-impaired individual with a quiet, socially inactive lifestyle is less likely to be motivated for hearing aids than a similarly impaired individual who works full time and has an active social life. A thorough patient history and needs assessment, coupled with objective testing can more accurately identify hearing aid candidates than relying on degree of hearing loss alone. The authors of this article cite a study of Swiss hearing aid use and satisfaction, stating that in Switzerland, hearing aid candidacy is “based on the degree of social and emotional handicap due to hearing loss” and that the dispensing process focuses on ongoing counseling and care after the fitting.  This study reported high rates of long-term hearing aid use and satisfaction, where 97% of Swiss hearing aid owners reported using their hearing aids and only 3% were non-users (Bertoli, 2009).

It makes sense to advise unmotivated individuals to assess their difficulties, making note of every time they ask for repetition, misunderstand a word or sentence, or smile and “fake” their way through a conversation. I instruct patients to consider whether their hearing loss causes them to avoid places or situations that they might otherwise enjoy or if the hearing loss affects their ability to perform important work-related or social activities.  With a little patience and attention, most people can determine the point at which they are ready to proceed with a hearing aid purchase. Self-recognition of need is strongly associated with eventual hearing aid uptake and use (Vestergaard-Knudsen et al., 2010), meaning that a person who returns for a consultation after taking time to evaluate their difficulties is more likely to keep their hearing aids and follow through with proper use and care.

Even as testing techniques and prevalence data improve our ability to identify those with hearing impairment and those at risk, there remain barriers to hearing aid use. Consistent representation of hearing aids as medical devices that are fitted by clinical professionals may improve the perception and attitudes of the general public. Unbundled pricing may lower the cost barrier by making the initial purchase more affordable and concomitantly emphasizing the value of follow-up care. Finally, development and adherence to a thorough fitting protocol will ensure that those who do purchase hearing aids will receive a well-prescribed medical device and become an example of success to others.



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Kochkin, S. (2010). MarkeTrak VIII: Customer satisfaction with hearing aids is slowly increasing. Hearing Journal 63(1), 11-19.

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McCormack, A. & Fortnum, H. (2013). Why do people fitted with hearing aids not wear them? International Journal of Audiology 52, 360-368.

Plomp, R. & Mimpen, A. (1979). Speech reception threshold for sentences as a function of age and noise level. Journal of the Acoustical Society of America 66, 1333-1342.

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The most important factors behind directional microphone benefit

Keidser, G., Dillon, H., Convery, E. & Mejia, J. (2013). Factors influencing individual variation in perceptual directional microphone benefit. Journal of the American Academy of Audiology 24, 955-968.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Understanding conversation in noisy environments is one of the most common difficulties for individuals with hearing loss. Counseling and training in communication strategies can help listeners with hearing loss make use of supplemental cues to improve speech understanding in noise. However, no hearing aid feature or clinical intervention is as likely to improve the ability to function in noise as directional microphones. Directional microphones, usually twin microphone designs, offer small but helpful increases in the signal-to-noise ratio, facilitating more comfortable listening and an improved ability to understand speech and function in noisy everyday situations.

Directionality consistently demonstrates benefits to speech perception performance in laboratory studies but the amount of directional benefit achieved by subjects is highly variable, even in studies with similar methods and procedures (Freyaldenhoven et al., 2005). A number of factors have been studied and reports have indicated that variability in directional benefit was unrelated to age (Wu, 2010; O’Brien et al, 2009), degree or configuration of hearing loss (Jesperson & Olsen, 2003; Ricketts & Mueller, 2000) or vent size (Ricketts, 2000; O’Brien et al, 2009). Furthermore, laboratory studies may not always predict everyday performance (Walden et al., 2000; Cord et al., 2002; Cord et al., 2004) so it is unclear how numerous factors could converge to affect individual directional benefit in everyday hearing aid use.

Recently emerging evidence has suggested that cognitive capacity may affect a listener’s ability to make use of directional benefits. Working memory affected hearing aid users’ performance with regard to different compression time constants (Gatehouse et al., 2003; Cox & Xu, 2010) and spatial separation ability (Neher et al., 2009). Dawes et al (2010) reported that differences in hearing aid benefit were partly determined by performance on speed of processing, selective attention and switching tasks. Humes (2007) further reported that cognition may affect individual speech perception abilities in noise. Though cognition declines with age, the changes vary tremendously across individuals and cannot be predicted by age alone (Glisky, 2007), so age and cognition, though related, may affect hearing aid use and speech perception in different ways.

The primary goal of Keidser et al’s study was to investigate the factors that contribute to variability in perceptual directional microphone benefit as measured in the laboratory. Specifically, they were interested in the effects and interaction of three potential sources of variability: differences in the individual SNR achieved by physical directional benefit, differences in the ability to make use of SNR improvements and variability related to measurement error.

Fifty-nine subjects participated in the study. All had bilateral, mild-to-moderate, sensorineural hearing loss.  Age ranged from 54 to 91 years, with an average of 74 years. Of the 59 subjects, 51 had experience with amplification, whereas 8 had never worn hearing aids. For the purpose of the study, subjects were fitted with binaural, behind-the-ear hearing aids with dual-microphones and wide dynamic range compression. Advanced signal processing such as noise reduction and adaptive directionality was turned off. Hearing aids were programmed according to NAL-NL2 targets and had two programs: omnidirectional and directional.

Participants attended two experimental sessions. At the first session, subjects completed cognitive testing. First, they were administered subtests of the Test of Everyday Attention (TEA; Robertson et al., 1996) which uses real-life scenarios to measure auditory selective attention and speed of processing. Working memory was assessed using the Reading Span Test (RST; Daneman & Carpenter, 1980).  In the RST, sentences are presented on a computer screen and subjects indicate whether the sentence was meaningful or not, subjects must also recall either the first or last word of each sentence.

At the second session, hearing aids and earmolds were fitted and vent diameters were measured. The frequency range of amplification was measured, with the low frequency limit (f-amp) defined as the point at which real-ear insertion gain exceeded 3dB. The angle of the microphone ports was measured with reference to the loudspeaker axis. Speech in noise testing was completed, using the Australian Bamford-Kowal-Bench (BKB/A) sentences (Bench et al., 1979) in the presence of 8-talker babble from the NAL Speech and Noise for Hearing Aid Evaluation CD (Keidser et al, 2002). Speech was presented from a loudspeaker 1m in front of the subject. A constant level of uncorrelated multi-talker babble was presented from four loudspeakers surrounding the subject at a distance of 2m. Speech levels were adjusted to arrive at the SNR required to achieve 50% performance.

Following speech in noise testing, individual in-situ SNR levels were measured to determine how room acoustics may have affected hearing aid performance.  Individual 3D AI-DI measurements were obtained to ascertain the physical directional benefit for each subject in the test environment. The 3D AI-DI scores are directivity measurements weighted by the Articulation Index model, as measured in the center of a 3D array of 41 loudspeakers (Killion et al, 1998). In-situ SNR and 3D-AI-DI measures were computed for broadband (BB), low-frequency (LF, <2000Hz) and high-frequency (HF, >2000Hz) ranges.

Cognitive test scores were weakly correlated. The only auditory cognitive test, the ASA, was not correlated with audiological pure tone average (PTA) but was weakly correlated with age. For the physical measures, broadband (BB) and low-frequency (LF) in-situ SNRs were strongly correlated with each other. The low-frequency limit or f-amp, was highly correlated to the LF in-situ measures as well as to PTA and vent diameter. These correlations indicate that participants who had higher PTAs (more hearing loss) had smaller vent diameters, frequency responses extending further into the low-frequencies and more physical benefit from directional microphones at low frequencies.

The average perceptual directional benefit as measured by SRTn was 2.7dB, with a range from 0.3 to 5.3dB.  No participants showed negative effects of directionality.  When comparing benefit ranges in individual trials versus the mean of the three trials, effectively removing any variability attributable to random measurement errors, the range of benefit was reduced from around 9.2 dB to 5.0dB. Therefore, about half of the variation in directional microphone benefit was explained by measurement errors.  Variation in perceptual directional benefit was not correlated with age or configuration (slope) of hearing loss. Analysis of the cognitive and the in-situ measures of physical directionality showed that the only factors exerting a significant effect on perceptual benefit were LF 3D AI-DI, ASA scores and microphone angle.

With reference to the goals of their study, Keidser and her colleagues found that measurement error, physical directionality and the individual ability to make use of directional cues may contribute to variability in perceptual directional benefit. About half of the variability in measured perceptual directional benefit was attributable to measurement error associated with speech-in-noise testing. Measurement error could include head movements during testing causing brief head shadow effects, problems with speech test list equivalence (Dillon, 1982) and potential practice effects. The authors suggest that multiple measurements of perceptual directional benefit, in each test condition, should always be carried out in order to mitigate the effects of measurement error.

In agreement with previous reports, there was no direct relation between perceptual directional benefit and age, PTA or configuration of hearing loss, though there was a relation to vent diameter. Greater perceptual directional benefit was derived when greater physical directivity was achieved in the low frequencies, which was related to decreased vent diameter. This result is in agreement with previous work showing increased directional benefit with more occluded molds as compared to more open fittings (Ricketts, 2000; Fabry, 2006; Klemp & Dhar, 2008).

A more upward-pointing microphone angle was associated with improved perceptual directional benefit. This is in agreement with a report by Ricketts (2000) that showed increased physical directivity as microphone angle exceeded 20 degrees from the horizontal plane. The effect of microphone angle in the current study was small, accounting for only 4% of the variation. Because the interaction of microphone angle with other hearing aid and environmental characteristics is unknown, the authors do not recommend that clinicians deliberately fit hearing aids with microphones pointing upward.

The outcomes of this study emphasize the importance of low-frequency amplification to achieve optimal directional benefit. The lower limit of the amplification range as well as vent diameter have an effect on physical directivity that affects the perceptual benefit that can be derived from directionality. Thus, it is of particular importance for clinicians to not only select appropriate venting characteristics for each individual, but to ensure that the range of amplification is set in a manner that accounts for venting effects. Programming software requires the entry of acoustic parameters to guide frequency response characteristics, especially in the low frequency range; failure to enter the correct acoustic properties risks over or under amplifying the low-frequency range.

Of course there are many factors to consider when choosing venting, gain and output characteristics, but achieving optimal directional benefit should be considered among them.  Equalizing low-frequency gain in a directional program for use in noise may be advisable to achieve better directivity, but conversely, reduction of low-frequency gain in noise programs may be more comfortable and therefore more desirable for hearing aid users. Careful consideration of the way in which these variables interact for each individual is critical to their success with hearing aids in their daily activities.



Bench, R., Doyle, J., Daly, N. & Lind, C. (1979). The BKB/A Speech Reading (Lipreading) Test. Victoria: La Trobe University.

Cord, M., Surr, R., Walden, B. & Olson, L. (2002). Performance of directional microphone hearing aids in everyday life. Journal of the American Academy of Audiology 13 (6), 295-307.

Cord, M., Surr, R., Walden, B. & Dyrlund, O. (2004). Relationship between laboratory measures of directional advantage and everyday success with directional microphone hearing aids. Journal of the American Academy of Audiology 15(5), 353-364.

Cox, R. & Xu, J. (2010). Short and long compression release times: speech understanding, real world preferences and association with cognitive ability. Journal of the American Academy of Audiology 21(2), 121-138.

Daneman, M. & Carpenter, P. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior 19(4), 450-466.

Dawes, P., Munro, K., Kalluri, S., Nooraei, N. & Edwards, B. (2010). Older adults, hearing aids and listening effort. Paper presented at IHCON, August 11-15, Lake Tahoe.

Dillon, H. (1982). A quantitative examination of the sources of speech discrimination test score variability. Ear and Hearing, 3(2), 51-58.

Fabry, D. (2006). Facts vs. myths: the “skinny” on open-fit hearing aids. Hearing Review 13, 20-25.

Freyaldenhoven, M., Nabelek, A., Burchfield, S. & Thelin, J. (2005). Acceptable noise level as a measure of directional hearing aid benefit. Journal of the American Academy of Audiology 16(4), 228-236.

Gatehouse, S., Naylor, G. & Elberling, C. (2003). Benefits from hearing aids in relation to the interaction between the user and the environment. International Journal of Audiology 42 (Suppl. 1), S77-S85.

Glisky, E. (2007). Changes in cognitive function in human aging. In: Riddle DR, ed. Brain Aging: Models, Methods and Mechanisms. Boca Raton, FL: CRC Press, chpt. 1.

Humes, L. (2007). The contributions of audibility and cognitive factors to the benefit provided by amplified speech to older adults. Journal of the American Academy of Audiology 18(7), 590-603.

Jesperson, C. & Olsen, S. (2003). Does directional benefit vary systematically with omnidirectional performance? Hearing Review 10, 16-24, 62.

Keidser, G., Ching, T. & Dillon, H. (2002). The National Acoustic Laboratories’ (NAL) CDs of Speech and Noise for Hearing Aid Evaluation: normative data and potential applications. Australian New Zealand Journal of Audiology 24(1), 16-35.

Keidser, G., Dillon, H., Convery, E. & Mejia, J. (2013). Factors influencing individual variation in perceptual directional microphone benefit. Journal of the American Academy of Audiology 24, 955-968.

Killion, M., Schulein, R. & Christensen, L. (1998). Real-world performance of an ITE directional microphone. Hearing Journal 51(4), 24-38.

Klemp, E. & Dhar, S. (2008). Speech perception in noise using directional microphones in open canal hearing aids. Journal of the American Academy of Audiology 19(7), 571-578.

Neher, T., Behrens, T. & Carlile, S. (2009). Benefit from spatial separation of multiple talkers in bilateral hearing aid users: effects of hearing loss, age and cognition. International Journal of Audiology 48 (11), 758-774.

O’Brien, A., McLelland, M. & Keidser, G. (2009). The Effect of Asymmetric Directionality on Speech Recognition in Noise. NAL Report 019. Sydney: National Acoustic Laboratories.

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Acclimatizing to hearing aids may not mean what you think it means

Dawes, P., Munro, K., Kalluri, S., & Edwards, B. (2014). Acclimatization to hearing aids. Ear and Hearing, Published Ahead-of-Print.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

New patients frequently report that their new hearing aids sound tinny, metallic, loud, or unnatural. The clinical audiologist recognizes that these comments will decrease in frequency with time. A process often described as acclimatization: a reaction to new hearing aids that occurs because the patient has adjusted to hearing sound filtered by their hearing loss. When amplification is introduced, the subsequent increase in audibility and loudness perception is unfamiliar and therefore unnatural.

A smooth transition to hearing aid use can be achieved through counseling prior to fitting, preparing the individual for a period of unnatural sound quality. At the fitting, the instruments can be set below prescribed target, allowing the listener a more comfortable period of adjustment. Most individuals will accept increased gains, approaching prescribed target over 2 or 3 months. Some patients, however, require a much longer period of acclimatization of one to two years (Keidser, et al., 2008).

In addition to changes in the preferred gain of new hearing aid users, other improvements due to acclimatization have been proposed: speech discrimination over time (Bentler, et al.1993a, Gatehouse, 1992), subjective benefit and sound quality over time (Bentler, et al., 1993b; Ovegard, et al., 1997) and loudness perception and intensity discrimination over time (Olsen, et al., 1999; Philibert et al., 2002). Most of these studies reported small but significant acclimatization effects; while others found no significant differences between new and experienced hearing aid users (Smeds et al, 2006a, 2006b).

Ultimately, there is little agreement on the definition of this effect and even less agreement in the methods that quantify these changes. A high degree of response variability is usually noted, indicating that several factors (degree, etiology, and configuration of hearing loss) may contribute to the adjustment that is experienced by new hearing aid users.

Dawes and his colleagues outlined a number of goals for their study:  First, they hoped to determine if there is an acclimatization effect for aided speech recognition with current, nonlinear hearing aids and if there is a difference between unilateral and bilateral fittings. Second, they wanted to know if new hearing aid users’ self-reports would indicate a period of acclimatization. Third, they sought to determine if acclimatization could be predicted by the degree of hearing loss, prior hearing aid use or cognitive capacity.

Forty-nine subjects participated in the study, recruited from four audiology clinics. There were 16 new unilateral hearing aid users, 16 new bilateral users and 17 experienced users, including 8 bilateral and 9 unilateral users. Experienced subjects used their own hearing aids and new users were fitted with BTE or CIC instruments with comparable circuit technology.  New instruments were fitted to NAL-NL1 targets and verified with real-ear measurements. Newly-fitted subjects had a few days of hearing aid use prior to commencement of the study and were allowed gain adjustments only if necessary due to discomfort with prescribed gain levels.

To measure speech recognition, a 4-alternative forced-choice procedure was used, in which listeners were asked to select one word from a closed set of four rhyming words, in response to the prompt, “Can you hear the word X clearly?” In addition to the speech recognition test, subjects completed the Spatial, Speech and Qualities of Hearing Questionnaire – Difference version (SSQ-D; Gatehouse & Noble, 2004), as well as two measures of cognitive processing. The SSQ-D was administered after 12 weeks and allowed the subjects to judge their own changes in performance and listening effort with the hearing aids over the course of the study.

Two cognitive tests were administered. The first, a visual reaction time task, required participants to watch digits presented on a computer monitor and press the corresponding numbers on a keypad as quickly as possible. Responses were scored as correct or incorrect and response times were measured in milliseconds. Working memory was also evaluated, using the Digits Backwards subtest from the Weschler Adult Intelligence Scale – III (WAIS-III; Wechsler, 1997).  Subjects listened to lists of digits and were asked to repeat them in reverse order. Lists increased in length as the test progressed and responses were correct if all digits were repeated in the correct order.

In all test conditions, variability was high and a small improvement was noted over time, likely due to practice effects. The mean SNR required to achieve 50% performance did not differ between new unilateral and new bilateral hearing aid users, but experienced users required significantly more favorable SNRs to achieve this level of performance, compared to new users. This was attributed to the older average age and poorer hearing thresholds of the experienced user group.

For the new user groups, if acclimatization occurred it was expected that performance would improve in aided conditions over time. Instead there were small trends of improvements in unaided and aided conditions. For unilateral users, the trend was noted in the fitted ear, whereas for bilateral users, small improvements were noted for both ears.  Of all the variables studied, the only one to have a significant effect on performance was time, which yielded a small consistent improvement across groups and listening conditions.  When place, manner and voicing errors were analyzed, there was no significant difference for type of error, nor was there a significant interaction with the other variables of group, aiding, ear or presentation level.

Because of the high variability in responses, correlations were measured for effects of hearing aid usage, degree of hearing loss, cognitive capacity, and a change in audibility referred to as “stimulus novelty”. For new hearing aid users, there was no significant correlation between the change speech recognition scores, severity of hearing loss, cognitive test score, or hearing aid variables. Older age was only correlated with slower reaction time scores and a higher amount of time spent in quiet conditions. There were no significant correlations for SSQ-D scores and change in aided performance in any of the listening conditions. Disparate SSQ-D scores did indicate that new hearing aid users perceived improvements over the course of the study, whereas experienced users did not.

Though there were small increases in speech recognition performance over time in all conditions, this was consistent with a practice effect and was not taken as evidence for acclimatization. Self-reports from the SSQ-D showed that new users experienced improvements with amplification that were significantly greater than those reported for experienced users. It is not surprising that SSQ-D scores might still show improvement, as the SSQ-D probes subjective perceptions of performance, including listening effort and sound quality. These elements may well improve with consistent use of new hearing aids even if actual speech recognition has not changed significantly. Improved audibility may allow the listener to function well in everyday environments with significantly less effort, making a positive impression on the listener, more so than small but measurable improvements in word recognition.

Another potential explanation for the lack of agreement between objective and subjective measures in this study could be related to the actual comparison that was made by the subjects when the responded to the SSQ-D items.  Because new users probably experienced noticeable benefits from the hearing aids, they may have had trouble comparing their performance immediately post-fitting versus 12 weeks later and may have inadvertently compared pre-fitting and post-fitting performance, yielding a larger SSQ-D score.

Though the results of this study did not support an acclimatization effect for speech recognition, they do not rule out the existence of acclimatization altogether. Preferred gain, perceived listening effort, and sound quality improvements, among other effects, may well occur for most new hearing aid users, to varying degrees based on degree of hearing loss, duration of prior hearing loss and prior experience with hearing aids.

The subjects in this study were fitted with either BTE or CIC hearing aids but the hearing aid style was not examined with regard to acclimatization. CIC users often experience occlusion and adjustment to their own voices in the early days of hearing aid use; much more so than BTE users who probably have less occlusion than commonly found with CIC hearing aids. Whether this could have an impact on speech recognition acclimatization is questionable, but it could have affect subjective reports. Similarly, individuals using hearing aid features such as frequency-lowering or wireless routing of signal may demonstrate other perceptual learning or acclimatization effects.

Perhaps the greatest finding of this study was the contrast between measurable outcomes in the domain of subjective spatial perception and traditional measures of speech recognition. Many failed attempts to document acclimatization have focused on speech recognition or loudness perception rather than probing the patient’s perception of their acoustic environment—something achieved with the SSQ-D. The apparent sensitivity of this measure should direct future experimental design in this area. For the practicing clinician, this contrast can aid in developing counseling approaches: it’s clear that speech recognition won’t change over time, but the complexity or overwhelming nature of the acoustic environment may become simpler with time.


Bentler, R.A., Niebuhr, D.P., Getta, J.P. & Anderson, C.V. 1993a. Longitudinal study of hearing aid effectiveness. I. Objective measures.  Journal of Speech and Hearing Research 36, 808-819.

Gatehouse, S. 1992. The time course and magnitude of perceptual acclimatization to frequency responses: Evidence from monaural fitting of hearing aids. Journal of the Acoustical Society of America 92, 1258-1268.

Gatehouse, S. & Noble, W. (2004). The Speech, Spatial and Qualities of Hearing Scale (SSQ). International Journal of Audiology 43, 85-99.

Keidser, G., O’Brien, A., Carter, L., McLelland, M., and Yeend, I. (2008). Variation in Preferred Gain with Experience for Hearing-Aid Users.  International Journal of Audiology 47 (10), 621 – 635.

Munro, K.J. & Lutman, M.E. 2003. The effect of speech presentation level on measurement of auditory acclimatization to amplified speech.  Journal of the Acoustical Society of America, 114, 484-495.

Ovegard, A., Lundberg, G., Hagerman, B., Gabrielsson, A., Bengtsson, M. 1997. Sound quality judgment during acclimatization of hearing aid. Scandinavian Audiology, 26, 43-51.

Palmer, C.V., Nelson, C.T. & Lindley, G.A. 1998. The functionally and physiologically plastic adult auditory system. Journal of the Acoustical Society of America, 103, 1705-1721.

Philibert, B., Collet, L., Vesson, J.F. & Veuillet, E. 2002. Intensity-related performances are modified by long-term hearing aid use: A functional plasticity? Hearing Research, 165, 142-151.

Philibert, B., Collet, L., Vesson, J.F. & Veuillet, E. 2005. The auditory acclimatization effect in sensorineural hearing-impaired listeners: Evidence for functional plasticity. Hearing Research, 205, 131-142.

Ronnberg, J., Rudner, M. & Foo, C. (2008). Cognition counts: A working memory system for ease of language understanding (ELU). International Journal of Audiology 47 (Suppl 2), S99-105.

Saunders, G.H. & Cienkowski, K. (1997). Acclimatization to hearing aids. Ear and Hearing 18, 129-139.

Smeds, K., Keidser, G., Zakis, J., Dillon, H., Leijon, A. 2006a. Preferred overall loudness. I. Sound field presentation in the laboratory. International Journal of Audiology, 45, 12-25.

Smeds, K., Keidser, G., Zakis, J., Dillon, H., Leijon, A. 2006b. Preferred overall loudness. II. Listening through hearing aids in field and laboratory tests. International Journal of Audiology, 45, 12-25.

Taubman, L., Palmer, C. & Durrant, J. (1999). Accuracy of hearing aid use time as reported by experienced hearing aid wearers. Ear and Hearing 20, 299-305.

Wechsler, D. (1997). Wechsler Adult Intelligence Scale (3rd ed.) Oxford: Pearson Assessment.

Willott, J.F. 1996. Physiological plasticity in the auditory system and its possible relevance to hearing aid use, deprivation effects, and acclimatization. Ear and Hearing, 17, 66S-77S.

Yund, E.W., Roup, C.M. & Simon, H.J. (2006). Acclimatization in wide dynamic range multichannel compression and linear amplification hearing aids.  Journal of Rehabilitation Research and Development 43, 517-536.

On the Prevalence of Cochlear Dead Regions

Pepler, A., Munro, K., Lewis, K. & Kluk, K. (2014). Prevalence of Cochlear Dead Regions in New Referrals and Existing Adult Hearing Aid Users. Ear and Hearing 20(10), 1-11.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Cochlear dead regions are areas in which, due to inner hair cell and/or nerve damage, responses to acoustic stimuli occur not at the area of peak basilar membrane stimulation but instead occur at adjacent regions in the cochlea. Professor Brian Moore defined dead regions as a total loss of inner hair cell function across a limited region of the basilar membrane (Moore, et al., 1999b). This hair cell loss does not result in an inability to perceive sound at a given frequency range, rather the sound is perceived via off-place or off-frequency listening, a spread of excitation to adjacent regions in the cochlea where inner hair cells are still functioning (Moore, 2004).  Because the response is spread across a broad tonotopic area, individuals with cochlear dead regions may perceive pure tones as “clicks”, “buzzes” or “whooshes”.

Cochlear dead regions are identified and measured by a variety of masking techniques. The most accurate method is the calculation of psychophysical tuning curves (PTCs), originally developed to measure frequency selectivity (Moore & Alcantara 2001). A PTC plots the level required to mask a stimulus frequency as a function of the masker frequency. For a normally hearing ear, the PTC peak will align with the point at which the stimulus can be masked by the lowest level masker.  In ears with dead regions, the tip of the PTC is shifted off of the signal frequency to indicate that the signal is being detected in an adjacent region. Though PTCs are an effective method of identifying and delineating the edges of cochlear dead regions, they are time consuming and ill-suited to clinical use.

The test used most frequently for clinical identification of cochlear dead regions is the Threshold Equalizing Test (TEN; Moore et al., 2000; 2004). The TEN test was developed with the idea that tones detected by off-frequency listening, in ears with dead regions, should be easier to mask with broadband noise than they would in ears without dead regions. With the TEN (HL) test, masked thresholds are measured across the range of 500Hz to 4000Hz, allowing the approximate identification of a cochlear dead region.

There are currently no standards for clinical management of cochlear dead regions. Some reports suggest that affect speech, pitch, loudness perception, and general sound quality (Vickers et al., 2001; Baer et al., 2002; Mackersie et al., 2004; Huss et al., 2005a; 2005b). Some researchers have specified amplification characteristics to be used with patients with diagnosed dead regions, but there is no consensus and different studies have arrived at conflicting recommendations. While some recommend limiting amplification to a range up to 1.7 times the edge frequency of the dead region (Vickers et al., 2001; Baer et al., 2002), others advise the use of prescribed settings and recommend against limiting high frequency amplification (Cox et al., 2012; see link for a review).  Because of these conflicting recommendations, it remains unclear how clinicians should modify their treatment plans, if at all, for hearing aid patients with dead regions.

Previous research on the prevalence of dead regions has reported widely varying results, possibly due to differences in test methodology or subject characteristics. In a study of hearing aid candidates, Cox et al. (2011) reported a dead region prevalence of 31%, but their strict inclusion criteria likely missed individuals with milder hearing losses, so their prevalence estimate may be different from that of hearing aid candidates at large. Vinay and Moore (2007) reported higher prevalence of 57% in a study that did include individuals with thresholds down to 15dB HL at some frequencies, but the median hearing loss of their subjects was higher than that of the Cox et al. study, which likely impacted the higher prevalence estimate in their subject group.

In the study being reviewed, Pepler and her colleagues aimed to determine how prevalent cochlear dead regions are among a population of individuals who have or are being assessed for hearing aids. Because dead regions become more likely as hearing loss increases, and established hearing aid patients are more likely to have greater degrees of hearing loss, they also investigated whether established hearing aid patients would be more likely to have dead regions than newly referred individuals.  Finally, they studied whether age, gender, hearing thresholds or slope of hearing loss could predict the presence of cochlear dead regions.

The researchers gathered data from a group of 376 patients selected from the database of a hospital audiology clinic in Manchester, UK. Of the original group, 343 individuals met inclusion criteria; 193 were new referrals and 150 were established patients and experienced hearing aid users.  Of the new referrals, 161 individuals were offered and accepted hearing aids, 16 were offered and declined hearing aids and 16 were not offered hearing aids because their losses were of mild degree.  The 161 individuals who were fitted with new hearing aids were referred to as “new” hearing aid users for the purposes of the study. All subjects had normal middle ear function and otoscopic examinations and on average had moderate sensorineural hearing losses.

When reported as a proportion of the total subjects in the study, Pepler and her colleagues found dead region prevalence of 36%.  When reported as the proportion of ears with dead regions, the prevalence was 26% indicating that some subjects had dead regions in one ear only. Follow-up analysis on 64 patients with unilateral dead regions revealed that the ears with dead regions had significantly greater audiometric thresholds than the ears without dead regions. Only 3% of study participants had dead regions extending across at three or more consecutive test frequencies. Ears with contiguous dead regions had greater hearing loss than those without.  Among new hearing aid users, 33% had dead regions while the prevalence was 43% among experienced hearing aid users. On average, the experienced hearing aid users had poorer audiometric thresholds on average than new users.

Pepler and colleagues excluded hearing losses above 85dB HL because effective TEN masking could not be achieved. Therefore, dead regions were most common in hearing losses from 50 to 85dB HL, though a few were measured below that range. There were no measurable dead regions for hearing thresholds below 40dB HL. Ears with greater audiometric slopes were more likely to have dead regions, but further analysis revealed that only 4 kHz thresholds had a significant predictive contribution and the slopes of high-frequency hearing loss only predicted dead regions because of the increased degree of hearing loss at 4 kHz.

Demographically, more men than women had dead regions in at least one ear, but their audiometric configurations were different: women had poorer low frequency thresholds whereas men had poorer high frequency thresholds. It appears that the gender effect actually due to the difference in audiometric configuration, specifically the men’s poorer high frequency thresholds. A similar result was reported for the analysis of age effects. Older subjects had a higher prevalence of dead regions but also had significantly poorer hearing thresholds.  Though poorer hearing thresholds at 4kHz did slightly increase the likelihood of dead regions, regression analysis of the variables of age, gender and hearing thresholds found that none of these factors were significant predictors.

Pepler et al’s prevalence data agree with the 31% reported by Cox et al (2012), but are lower than that reported by Vinay and Moore (2007), possibly because the subjects in the latter study had greater average hearing loss than those in the other studies.  But when Pepler and her colleagues used similar inclusion criteria to the Cox study, they found a prevalence of 59%, much higher than the report by Cox and her colleagues and likely due to the exclusion of subjects with normal low frequency hearing in the Cox study. The authors proposed that Cox’s exclusion of subjects with normal low frequency thresholds could have reduced the overall prevalence by increasing the proportion of subjects with metabolic presbyacusis and eliminating some subjects with sensory presbyacusis—sensory presbyacusis is often associated with steeply sloping hearing loss and involves atrophy of cochlear structures (Shuknecht, 1964).

 In summary:

The study reported here shows that roughly a third of established and newly referred hearing aid patients are likely to have at least one cochlear dead region, in at least one ear. A very low proportion (3% reported here) of individuals are likely to have dead regions spanning multiple octaves. The only factor that predicted the presence of dead regions was hearing threshold at 4 kHz.

On the lack of clinical guidance:

As more information is gained about prevalence and risk factors, what remains missing are clinical guidelines for management of hearing aid users with diagnosed high-frequency dead regions. Conflicting recommendations have been proposed for either limiting high frequency amplification or preserving high frequency amplification and working within prescribed targets. The data available today suggest that prevalence of contiguous multi-octave dead regions is very low and a further subset of hearing aid users with contiguous dead regions experience any negative effects of high-frequency amplification. With consideration to these observations, it seems prudent that the prescription of high-frequency gain should adhere to the prescribed targets for all patients at the initial fitting. Any reduction to high-frequency gains should be managed as a result of subjective feedback from the patient after they have completed a trial period with their hearing aids.

On frequency lowering and dead regions:

Some clarity is required regarding the role of frequency lowering and the treatment of cochlear dead regions. Because acoustic information in speech extends out to 10 kHz and because most hearing aid frequency responses roll off significantly after 4-5 kHz, the mild prescription of frequency lowering can be beneficial to many hearing aid users. It must be noted that the benefits of this technology arise largely from the acoustic limitations of the device and not the presence or absence of a cochlear dead region. There are presently no recommendations for the selection of frequency lowering parameters in cases of cochlear dead regions. In the absence of these recommendations, the best practice for the prescription of frequency lowering would follow the same guidelines as any other patient with hearing loss; validation and verification should be performed to document benefit with the algorithm and identify appropriate selection of algorithm parameters.

On the low-frequency dead region: 

The effects of low-frequency dead regions are not well studied and may have more significant impact on hearing aid performance.  Hornsby (2011) reported potential negative effects of low frequency amplification if it extends into the range of low-frequency dead regions (Vinay et al., 2007; 2008). In some cases performance decrements reached 30%, so the authors recommended using low-frequency gain limits of 0.57 times the low-frequency edge of the dead region in order to preserve speech recognition ability. Though dead regions are less common in the low frequencies than in the high frequencies, more study on this topic is needed to determine clinical testing and treatment implications.


Baer, T., Moore, B. C. and Kluk, K. (2002). Effects of low pass filtering on the intelligibility of speech in noise for people with and without dead regions at high frequencies. Journal of the Acoustical Society of America 112(3 Pt 1), 1133-44.

Cox, R., Alexander, G., Johnson, J., Rivera, I. (2011). Cochlear dead regions in typical hearing aid candidates: Prevalence and implications for use of high-frequency speech cues. Ear and Hearing 32(3), 339 – 348.

Cox, R.M., Johnson, J.A. & Alexander, G.C. (2012).  Implications of high-frequency cochlear dead regions for fitting hearing aids to adults with mild to moderately severe hearing loss. Ear and Hearing 33(5), 573-87.

Hornsby, B. (2011) Dead regions and hearing aid fitting. Ask the Experts, Audiology Online October 3, 2011.

Huss, M. & Moore, B. (2005a). Dead regions and pitch perception. Journal of the Acoustical Society of America 117, 3841-3852.

Huss, M. & Moore, B. (2005b). Dead regions and noisiness of pure tones. International Journal of Audiology 44, 599-611.

Mackersie, C. L., Crocker, T. L. and Davis, R. A. (2004). Limiting high-frequency hearing aid gain in listeners with and without suspected cochlear dead regions. Journal of the American Academy of Audiology 15(7), 498-507.

Moore, B., Huss, M. & Vickers, D. (2000). A test for the diagnosis of dead regions in the cochlea. British Journal of Audiology 34, 205-224.

Moore, B. (2004). Dead regions in the cochlea: Conceptual foundations, diagnosis and clinical applications. Ear and Hearing 25, 98-116.

Moore, B. & Alcantara, J. (2001). The use of psychophysical tuning curves to explore dead regions in the cochlea. Ear and Hearing 22, 268-278.

Moore, B.C., Glasberg, B. & Vickers, D.A. (1999b). Further evaluation of a model of loudness perception applied to cochlear hearing loss. Journal of the Acoustical Society of America 106, 898-907.

Pepler, A., Munro, K., Lewis, K. & Kluk, K. (2014). Prevalence of Cochlear Dead Regions in New Referrals and Existing Adult Hearing Aid Users. Ear and Hearing 20(10), 1-11.

Schuknecht HF. Further observations on the pathology of presbycusis. Archives of Otolaryngology 1964;80:369—382

Vickers, D., Moore, B. & Baer, , T. (2001). Effects of low-pass filtering on the intelligibility of speech in quiet for people with and without dead regions at high frequencies. Journal of the Acoustical Society of America 110, 1164-1175.

Vinay and Moore, B. C. (2007). Speech recognition as a function of high-pass filter cutoff frequency for people with and without low-frequency cochlear dead regions. Journal of the Acoustical Society of America 122(1), 542-53.

Vinay, Baer, T. and Moore, B. C. (2008). Speech recognition in noise as a function of high pass-filter cutoff frequency for people with and without low-frequency cochlear dead regions. Journal of the Acoustical Society of America 123(2), 606-9.

Should you prescribe digital noise reduction to children?

Pittman, A. (2011). Age-related benefits of digital noise reduction for short term word learning in children with hearing loss. Journal of Speech, Language and Hearing Research 54, 1448-1463.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

A child’s ability to learn new words has important implications for language acquisition, mental and social development as well as academic achievement.  How easily a child acquires new vocabulary words can be affected by numerous factors, including age, working memory and current vocabulary (Alt, 2010). Hearing loss is known to adversely affect children’s ability to learn new words and the more severe the loss, the more significant the effect on word learning (Pittman, et al., 2005; Blamey et al., 2001). The effect of hearing loss on word learning may be related to a decreased ability to encode the degraded stimuli into working memory. Indeed, in a study with normal-hearing and hearing-impaired children, Pittman found that word stimuli that were modified with narrowed bandwidths were harder for children to learn (Pittman, 2008). Similar results indicating that degraded perception adversely affects children’s phonological processing have been reported elsewhere (Briscoe, et al., 2001).

In many everyday listening situations, speech must be perceived in the presence of noise or other competing sounds. Noise can degrade the speech information, making words more difficult to encode into working memory and identify correctly. Individuals with hearing loss are more adversely affected by the presence of background noise (Kochkin, 2002; McCoy et al., 2005; Picou et al., 2013), which is of particular concern when the effects of noise on word learning are considered. Hearing aids can at least partially mitigate effects of background noise with the use advanced signal processing like directional microphones and digital noise reduction (DNR). However, little evidence exists to support beneficial effects of DNR on word learning. Pittman suggests that there is reason for concern as DNR could impose negative effects on word learning because of reductions in overall amplification. Additionally, the effect of DNR on connected speech, which offers semantic and syntactic context, may be very different than the effects on isolated word learning, so the everyday experience of hearing aid users could be different from laboratory results that measured perception of isolated words.

This study examined how DNR affects word learning in hearing-impaired children with hearing aids. The authors presented these hypotheses:

1)              Word learning would decrease in noise for children with normal hearing and those with hearing loss.

2)              Word learning rates would slow in noise, due to the reduction in overall amplification imposed by DNR.

Forty-one children with normal hearing and 26 participants with mild-to-moderate hearing loss participated in the study. The treatment groups were comprised of two age sub-groups: a younger group of children from age 8-10 and a slightly older group of children from age 11-12. The children with hearing loss had been diagnosed at an average age of approximately 3 years and all but one wore personal hearing aids. Participants with hearing loss were fitted with BTE hearing aids programmed to DSL v5.0 targets, verified with real-ear measures and set with two programs. In Program 1, advanced signal processing features like noise reduction, impulse reduction, wind noise reduction and feedback management were turned off. In Program 2, these features remained disabled except for noise reduction, which was set to maximum.

Word learning was tested using nonsense words, presented in three sets of five words each. All were two-syllable words and each list contained words with the same vowels in the first and last syllables. Stimuli were presented in sound field by a female talker at a level of 50dB SPL and SNR of 0dB. Children were seated at a small table about one meter away from the speaker. Nonsense words were presented on a computer screen, along with five pictures of nonsense objects categorized as toys, flowers or aliens. The children were asked to select the appropriate picture to go with the word and were given positive reinforcement for selecting the correct picture. No reinforcement was provided for selecting the wrong picture. Children therefore learned the new words via a process of trial and error.

The first goal of the study was to examine the impact on noise on children’s word learning ability. Statistical analyses indicated that NH participants learned words faster than the HL participants did, older children learned faster than younger children and learning in quiet was faster than learning in noise. The presence of noise resulted in further decrements in performance for HL listeners, indicating that noise had a more deleterious effect on word learning in noise for participants with hearing loss than it did for normally hearing participants.

The second goal of the study was to determine if DNR affected word learning for children with hearing loss. When DNR trials were compared to quiet and noise trials, younger children performed the same in noise whether or not they were using DNR in their hearing aids. Performance for both noise conditions was significantly poorer than performance in quiet. In contrast, the performance of older participants improved with DNR, with DNR performance closely approximating performance in quiet.

When results from the word learning task were examined with reference to Peabody vocabulary scores, the results indicated that participants with hearing loss had lower vocabulary ages than the normally hearing participants. For the experimental tasks, normally hearing participants required fewer trials to reach 70% performance than the participants with hearing loss. Further analysis revealed that the age of identification, age of amplification and years of amplification use accounted for 85% of the variance, but follow-up tests revealed significant relations between word learning and age, but not word learning and hearing history. These results suggest that despite individual variability, word learning in noise was most related to the factors of age and vocabulary.

In sum, the results of this investigation suggest that DNR did not have an effect, positive or negative, on younger participants. It did improve performance for older children, however, regardless of their hearing history or years of amplification. The author points out that childrens’ speech perception in noise is known to improve with age (Elliott, 1979; Scollie, 2008) but the participants in this study demonstrated age effects only when DNR was used. It appears that the combination of DNR and greater vocabulary knowledge allowed the older listeners to demonstrate superior word learning.

There are many factors to consider when prescribing amplification characteristics for children. Word learning is a critical developmental process for children, with important implications for future social and academic accomplishments.  The documented beneficial effects of DNR on word learning in complex listening environments could be a strong motivator for selection in a pediatric hearing aid. In addition to potential word learning benefits, DNR could make amplification more comfortable in noisy conditions, thereby increasing the acceptance of hearing aids and expanding potential opportunities for communication and further word learning.

Some caution should be voiced in the selection of DNR for pediatric use. Many of these algorithms reduce frequency-specific hearing aid gains, presenting the opportunity to compromise audibility of some speech sounds when listening in noise. Prior to consideration of any DNR algorithm in pediatric populations, data should be presented that ensure the maintenance of speech audibility when that particular DNR algorithm is active and noise is presented at a levels typical of the child’s academic setting (see: Stelmachowicz et al., 2010).

The outcomes reported here provide general support for the use of DNR in school-age children. It must be clarified that the documented benefits do not suggest improved speech understanding, as this is not a function of the algorithm. Rather, the documented improvements in word learning most likely arise from the fact that noise in the absence of speech was reduced in level, reducing the effort required to listen to the individual words as they were presented.

For additional information on the prescription of hearing aid signal processing features in pediatric populations, please reference the 2013 Pedatric Amplification Guidelines, published by the American Academy of Audiology:



Alt, M. (2010). Phonological working memory impairments in children with specific language impairment: Where does the problem lie? Journal of Communication Disorders 44, 173-185.

Bentler, R., Wu, Y., Kettel, J. & Hurtig, R. (2008). Digital noise reduction: Outcomes from laboratory and field studies. International Journal of Audiology 47, 447-460.

Blamey, P., Sarant, J., Paatsch, L., Barry, J., Bow, C., Wales, R. & Rattigan, K. (2001). Relationships among speech perception, production, language, hearing loss and age in children with impaired hearing. Journal of Speech, Language and Hearing Research 44, 264-285.

Briscoe, J., Bishop, D. & Norbury, C. (2001). Phonological processing, language and literacy: a comparison of children with mild-to-moderate sensorineural hearing loss and those with specific language impairment. Journal of Child Psychology and Psychiatry and Allied Disciplines 42, 329-340.

Dunn, L. & Dunn, L. (2006). Peabody Picture Vocabulary Test – III. Circle Pines, MN:AGS.

Kochkin, S. (2002). MarkeTrak VI: 10-year customer satisfaction trends in the US hearing instrument market. The Hearing Review 9 (10), 14-25, 46.

McCoy, S.L., Tun, P.A. & Cox, L.C. (2005). Hearing loss and perceptual effort: downstream effects on older adults’ memory for speech. Quarterly Journal of Experimental Psychology A, 58, 22-33.

Picou, E.M., Ricketts, T.A. & Hornsby, B.W.Y. (2013). How hearing aids, background noise and visual cues influence objective listening effort. Ear and Hearing 34 (5).

Pittman, A., Lewis, D., Hoover, B. & Stelmachowicz, P. (2005). Rapid word learning in normal-hearing and hearing-impaired children. Effects of age, receptive vocabulary and high-frequency amplification. Ear and Hearing 26, 619-629.

Pittman, A. (2008).  Short-term word learning rate in children with normal hearing and children with hearing loss in limited and extended high-frequency bandwidths. Journal of Speech, Language and Hearing Research 51, 785-797.

Pittman, A. (2011). Age-related benefits of digital noise reduction for short term word learning in children with hearing loss. Journal of Speech, Language and Hearing Research 54, 1448-1463.

Ng, E., Rudner, M., Lunner, T., Syskind Pedersen, M. & Rönnberg, J. (2013).  Effects of noise and working memory capacity on memory processing of speech for hearing aid users. International Journal of Audiology, Early Online: 1–9

Ricketts, T. & Hornsby, B. (2005). Sound quality measures for speech in noise through a commercial hearing aid implementing digital noise reduction. Journal of the American Academy of Audiology 16, 270-277.

Sarampalis, A., Kalluri, S. & Edwards, B. (2009). Objective measures of listening effort: effects of background noise and noise reduction. Journal of Speech Language and Hearing Research 52, 1230-1240.

Stelmachowicz, P., Lewis, D., Hoover, B., Nishi, K., McCreery, R. & Woods, W. (2010). Effects of digital noise reduction on speech perception for children with hearing loss. Ear and Hearing 31, 245-355.

The Top 5 Hearing Aid Research Articles from 2013!

1) The Clinical Practice Guidelines in Pediatric Amplification

After a 10-year wait, the guidelines for prescription of hearing aids to children were updated in 2013—making them the most modern of any peer-reviewed guidelines. There is little doubt that these recommendations will impact future publication and fitting protocols at clinical sites around the world. The guidelines are freely available at the link below.

American Academy of Audiology. (2013). Clinical Practice Guidelines Pediatric Amplification. Reston, VA: Ching, T., Galster, J., Grimes, A., Johnson, C., Lewis, D., McCreery, R…Yoshinago-Itano, C.

2) Placebo effects in hearing aid trials are reliable

This article echoes publications from the early 2000’s (e.g., Bentler et al., 2003) that reported on blinded comparisons of analog and digital hearing aids. In those early studies, participants showed clear bias when primed to believe that option ‘A’ was a higher technology than option ‘B’. That early work was more focused on comparing technologies than this insightful report on placebo effects. Dawes and colleagues share an important reminder that placebo is real and should be accounted for in experimental design, whenever possible.

Dawes, P., Hopkins, R., & Munro, K. (2013). Placebo effects in hearing aid trials are reliable. International Journal of Audiology, 52(7), 472-477.

3) Effects of hearing aid use on listening effort and mental fatigue

In the last few years, a number of research audiologists and hearing scientists have worked to document relationships between cognitive capacity, listening effort, and hearing aid use. An undertone of these efforts has been the assumption that a person with hearing loss will be less fatigued when listening with hearing aids. This article is one of the first published attempts at clearly documenting this fatiguing effect.

Hornsby, B.W. (2013). Effects of hearing aid use on listening effort and mental fatigue associated with sustained speech processing demands. Ear & Hearing, 34(5), 523-534.

4) Characteristics of hearing aid fittings in infants and young children

The recent publication of updated pediatric fitting guidelines leads one to wonder how well fundamental aspects of these recommendations are being followed. This report from McCreery and colleagues is a clear indication that superior pediatric hearing care is uncommon and most often found in large pediatric medical centers. They also reinforce the consideration that consistent care from a single center may result in the most prescriptively appropriate hearing aid fitting.

McCreery, R., Bentler, R., & Roush, P. (2013). Characteristics of hearing aid fittings in infants and young children. Ear & Hearing, 34(6), 701-710.

5) The Style Preference Survey (SPS): a report on psychometric properties and a cross-validation experiment

Closing out the Top 5: this article warrants high regard for rigor in design and quality of reporting. The authors delivered an article that will educate future researchers on the development and validation of questionnaires. Beyond this utility, the results are some of the first to identify the dimensions of preference that underlie the well-established bias toward preference of open-canal hearing aids.

Smith, S., Ricketts, T., McArdle, R., Chisolm, T., Alexander, G., & Bratt, G. (2013). Style preference survey: a report on the psychometric properties and a cross-validation experiement. Journal of the American Academy of Audiology, 24(2), 89-104.

Patients with higher cognitive function may benefit more from hearing aid features

Ng, E.H.N., Rudner, M., Lunner, T., Pedersen, M.S., & Ronnberg, J. (2013). Effects of noise and working memory capacity on memory processing of speech for hearing-aid users. International Journal of Audiology, Early Online, 1-9.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Research reports as well as clinical observations indicate that competing noise increases the cognitive demands of listening, an effect that is especially impactful for individuals with hearing loss (McCoy et al., 2005; Picou et al., 2013; Rudner et al., 2011).  Listening effort is a cognitive dimension of listening that is thought to represent the allocation of cognitive resources needed for speech recognition (Hick & Tharpe, 2002). Working memory, is a further dimension of cognition that involves the simultaneous processing and storage of information; its effect on speech processing may vary depending on the listening conditions (Rudner et al., 2011).

The concept of effortful listening can be characterized with the Ease of Language Understanding (ELU) model (Ronnberg, 2003; Ronnberg et al., 2008). In quiet conditions when the speech is audible and clear, the speech input is intact and is automatically and easily matched to stored representations in the lexicon. When speech inputs are weak, distorted or obscured by noise, mismatches may occur and speech inputs may need to be compared to multiple stored representations to arrive at the most likely match. In these conditions, allocation of additional cognitive resources, is required. Efficient cognitive functioning and large working memory capacity allows more rapid and successful matches between speech inputs and stored representations. Several studies have indicated a relationship between cognitive ability and speech perception: Humes (2007) found that cognitive function was the best predictor of speech understanding in noise and Lunner (2003) reported that participants with better working memory capacity and verbal processing speed had better speech perception performance.

Following the ELU model, hearing aids may allow listeners to match inputs and stored representations more successfully, with less explicit processing. Noise reduction, as implemented in hearing aids, has been proposed as a technology that may ease effortful listening. In contrast, however, it has been suggested that hearing aid signal processing may introduce unwanted artifacts or alter the speech inputs so that more explicit processing is required to match them to stored images (Lunner et al., 2009). If this is the case, hearing aid users with good working memory may function better with amplification because their expanded working memory capacity allows more resources to be applied to the task of matching speech inputs to long-term memory stores.

Elaine Ng and her colleagues investigated the effect of noise and noise reduction on word recall and identification and examined whether individuals were affected by these variables differently based on their working memory capacity. The authors had several hypotheses:

1. Noise would adversely affect memory, with poorer memory performance for speech in noise than in quiet.

2. Memory performance in noise would be at least partially restored by the use of noise reduction.

3. The effect of noise reduction on memory would be greater for items in late list positions because participants were older and therefore likely to have slower memory encoding speeds.

4. Memory in competing speech would be worse than in stationary noise because of the stronger masking effect of competing speech.

5. Overall memory performance would be better for participants with higher working memory capacity in the presence of noise reduction. This effect should be more apparent for late list items presented with competing speech babble.

Twenty-six native Swedish-speaking individuals with moderate to moderately-severe, high-frequency sensorineural hearing loss participated in the authors’ study. Prior to commencement of the study, participants were tested to ensure that they had age-appropriate cognitive performance. A battery of tests was administered and results were comparable to previously reported performance for their age group (Ronnberg, 1990).

Two tests were administered to study participants. First, a reading span test evaluated working memory capacity.  Participants were presented with a total of 24 three-word sentences and sub-lists of 3, 4 and 5 sentences were presented in ascending order. Participants were asked to judge whether the sentences were sensible or nonsense. At the end of each sub-list of sentences, listeners were prompted to recall either the first or final words of each sentence, in the order in which they were presented. Tests were scored as the total number of items correctly recalled.

The second test was a sentence-final word identification and recall (SWIR) test, consisting of 140 everyday sentences from the Swedish Hearing In Noise Test (HINT; Hallgren et al, 2006). This test involved two different tasks. The first was an identification task in which participants were asked to report the final word of each sentence immediately after listening to it.  The second task was a free recall task; after reporting the final word of the eighth sentence of the list, they were asked to recall all the words that they had previously reported. Three of seven tested conditions included variations of noise reduction algorithms, ranging from one similar to those implemented in modern hearing aids to an ‘ideal’ noise reduction algorithm.

Prior to the main analyses of working memory and recall performance, two sets of groups were created based on reading span scores, using two different grouping methods. In the first set, two groups were created by splitting the group at the median score so that 13 individuals were in a high reading span group and the remaining 13 were in a low reading span group. In the second set, participants who scored in the mid-range on the reading span test were excluded from the analysis, creating High reading span and Low reading span groups of 10 participants each. There was no significant difference between groups based on age, pure tone average or word identification performance, in any of the noise conditions. Overall reading span scores for participants in this study were comparable to previously reported results (Lunner, 2003; Foo, 2007).

Also prior to the main analysis, the SWIR results were analyzed to compare noise reduction and ideal noise reduction conditions. There was no significant difference between noise reduction and ideal noise reduction conditions in the identification or free recall tasks, nor was there an interaction of noise reduction condition with reading span score. Therefore, only the noise reduction condition was considered in the subsequent analyses.

The relationship between reading span score (representing working memory capacity) and SWIR recall was examined for all the test conditions. Reading span score correlated with overall recall performance in all conditions but one. When recall was analyzed as a function of list position (beginning or final), reading span scores correlated significantly with beginning (primacy) positions in quiet and most noise conditions. There was no significant correlation between overall reading span scores and items in final (recency) position in any of the noise conditions.

There were significant main effects for noise, list position and reading span group. In other words, when noise reduction was implemented, the negative effects of noise were lessened. There was a recency effect, in that performance was better for late list positions than for early list positions. Overall, the high reading span groups scored better than the low reading span groups, for both median-split and mid-range exclusion groups. The high reading span groups showed improved recall with noise reduction, whereas the low reading span groups exhibited no change in performance with noise reduction versus quiet.  The use of four-talker babble had a negative effect on late list positions, but did not affect items in other positions, suggesting that four-talker babble disrupted working memory more than steady-state noise. These analyses supported hypotheses 1, 2, 3 and 5, indicating that noise adversely affects memory performance (1), that noise reduction and list position interact with this effect (2,3) especially for individuals with high working memory capacity (5).

The results also supported hypothesis 4, which suggested that competing speech babble would affect memory performance more than steady state noise. Recall performance was significantly better in the presence of steady-state noise than it was in 4-talker babble. Though there was no significant effect of noise reduction overall, high reading span participants once again outperformed low reading span participants with noise reduction.

In summary, the results of this study determined that noise had an adverse effect on recall, but that this effect was mildly mitigated by the use of noise reduction. Four-talker babble was more disruptive to recall performance than was steady-state noise. Recall performance was better for individuals with higher working memory capacity. These individuals also demonstrated more of a benefit from noise reduction than did those with lower working memory capacity.

Recall performance is better in quiet conditions than in noise because presumably fewer cognitive resources are required to encode the speech input (Murphy, et al., 2000). Ng and her colleagues suggest that noise reduction helps to perceptually segregate speech from noise, allowing the speech input to be matched to stored lexical representations with less cognitive demand. So, noise reduction may at least partially reverse the negative effect of noise on working memory.

Competing speech babble is more likely to be cognitively demanding than steady-state noise (such as an air conditioner) because it contains meaningful information that is more distracting and harder to separate from the speech of interest (Sorqvist & Ronnberg, 2012). Not only is the speech signal of interest degraded by the presence of competing sound and therefore harder to encode, but additional cognitive resources are required to inhibit the unwanted or irrelevant linguistic information (Macken, 2009).  Because competing speech puts more demands on cognitive resources, it is more potentially disruptive than steady-state noise to perception of the speech signal of interest.

Unfortunately, much of the background noise encountered by hearing aid wearers is competing speech. The classic example of the cocktail party illustrates one of the most challenging situations for hearing-impaired individuals, in which they must try to attend to a proximal conversation while ignoring multiple conversations surrounding them. The results of this study suggest that noise reduction may be more useful in these situations for listeners with better working memory capacity; however, noise reduction should still be considered for all hearing aid users, with comprehensive follow-up care to make adjustments for individuals who are not functioning well in noisy conditions. Noise reduction may generally alleviate perceived effort or annoyance, allowing a listener to be more attentive to the speech signal of interest or to remain in a noisy situation that would otherwise be uncomfortable or aggravating.

More research is needed on the effects of noise, noise reduction and advanced signal processing on listening effort and memory in everyday situations. It is likely that performance is affected by numerous variables of the hearing aid, including compression characteristics, directionality, noise reduction, as well as the automatic implementation or adjustment of these features. These variables in turn combine with user-related characteristics such as age, degree of hearing loss, word recognition ability, cognitive capacity and more.


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