Next, we examined the relative effects of these nine significant variables on the CSS by fitting a multiple linear regression model using data from the entire sample (n = 122). The results showed that when all other variables were held constant, EHF thresholds and working memory scores were the only two significant predictors of the CSS, indicating that poorer EHF hearing and poorer working memory capacity were associated with reduced ability to understand speech in noise (see Table 2). This regression model accounted for 41% of the total variance [R2 = 0.41, F(9,112) = 7.57, p < 0.001].
We then fitted a second regression model using only EHF and RST to determine the relative effects of these two variables, and obtained a regression formula:
This formula was then assessed for its usefulness as a diagnostic criterion for predicting CSS.
First, we used the cross-validation results to assess whether our formula was able to accurately predict CSS using the RMSE method. The results yielded an RMSE of 0.60, which can be interpreted by noting that CSS, being the average of z scores, is expected to have a mean and SD of approximately 0 and 1, respectively. The RMSE value of 0.60 suggests that although this simple formula is likely inadequate for highly accurate prediction of CSS; it may be useful as a first-order approximation.
Next, we tested how well the regression formula predicted which participants were on the “low” end of the CSS performance scale using the AUC method. As shown in Figure 3, the AUC was equal to 0.76, meaning that for every 100 pairs of people, one “low” and one not “low”, the diagnostic criterion would correctly identify which person in each pair is “low” for 76 pairs.
Finally, we tested whether using one of the four EHF thresholds (9, 10, 11.2, or 12.5 kHz) instead of the average of the four EHF thresholds would yield similar results. We reasoned that since threshold testing at one, rather than four, frequency is more clinically expedient, it would be useful to know if any of the single frequencies was equivalent to the 4-frequency average. We repeated the cross-validation procedures for the four alternative models and found that results were slightly poorer for the 9 kHz and 11.2 kHz, and to a lesser extent 10 kHz models. These three frequencies were often not selected in the variable selection procedure indicating that they had less predictive value. However, using 12.5 kHz yielded RMSE and AUC results that were equally as good as, if not slightly better than, using the averaged EHF thresholds (see Table 3).
The purpose of this study was to devise a diagnostic criterion that could be used clinically for predicting or confirming “low” speech-in-noise performance in young and middle-aged adult listeners with normal hearing. The criterion we developed was a regression formula, based on EHF thresholds and RST results, and our results show that its ability to predict the CSS and identify “low” CSS performance was reasonable. Monte Carlo cross-validation results showed that the AUC was 0.76 indicating that the diagnostic criterion would correctly identify “low” CSS in approximately 76 out of every 100 pairs of people, where one was low and one was not, but it would also incorrectly identify some clients as “low” CSS when they were not. However, this situation would occur rarely, if at all, because people who do not self-perceive listening difficulties would be unlikely to seek hearing assessment in the first place. The RMSE of 0.60 shows that although there was some variation between the predicted and observed values, suggesting that the formula would not yield a highly accurate prediction of CSS, it was not so large that the predicted CSS would not be useful clinically in providing an approximate prediction. When we replaced the four-frequency average EHF with each of the stand-alone frequencies, 9, 10, 11.2 kHz, separately, results were slightly poorer, while the formula that included the 12.5 kHz threshold was equally as good as, if not slightly better than, the formula that used the four-frequency average EHF.
If used in clinical practice, our proposed diagnostic criterion would correctly identify or confirm “low” CSS in the majority of clients presenting with speech-in-noise problems. While it is acknowledged that not every client with normal hearing presenting with difficulty understanding speech in background noise will have elevated EHF thresholds or lower than average RST scores, our results show that using this diagnostic criterion (which is based on these two factors) would provide an evidence-based clinical explanation that would help a substantial proportion of clients to feel understood and likely result in a better clinical encounter than a standard hearing assessment currently provides (Pryce & Wainwright 2008; Pryce 2015).
Although EHF thresholds are not currently measured routinely, their diagnostic value is becoming increasingly recognized. Our results provide additional support of the link between elevated EHF thresholds and poorer speech-in-noise performance shown in several other recent studies of normal-hearing listeners (Badri et al. 2011; Liberman et al. 2016). Related to this are recent research results linking poorer EHF thresholds to increased levels of noise exposure in young adults (da Rocha et al. 2010; Liberman et al. 2016; Kumar et al. 2017; Prendergast et al. 2017) and the suggestion that noise damage may first appear in the EHF region (Somma et al. 2008; Le Prell et al. 2013; Sulaiman et al. 2014). Considered collectively, there is growing evidence that EHF thresholds in humans provide an early indicator of subclinical auditory damage that may coincide with noise-induced cochlear synaptopathy or other causes, for example, ototoxicity and aging. This has led some authors to recommended that EHF thresholds be included as part of standard testing (Rodríguez Valiente et al. 2016; Moore et al. 2017). Our findings provide further evidence that when clients present with difficulty understanding speech in noise (with or without a history or noise exposure) and are found to have normal thresholds for standard audiometric frequencies (≤20 dB HL, 0.25 to 4 kHz), best clinical practice would be to measure the client’s EHF thresholds, rather than reassure them that their hearing is “normal”.
Perhaps most importantly, use of the diagnostic criterion proposed here could provide a new avenue for counseling clients who present with speech-in-noise difficulty. For those who have a history of noise exposure, clinicians could point out that poor EHF thresholds are often associated with noise exposure and focus on the importance of avoiding excessive noise exposure, or using hearing protection when avoidance is not possible. For those clients without significant previous noise exposure, clinicians could discuss other possible causes of hearing damage such as ototoxicity, aging, and the interaction of these factors. For all clients, measuring one’s EHF thresholds provides a baseline to enable regular monitoring and early identification of hearing deterioration.
In time, future rehabilitation strategies may be developed on the basis of the diagnostic criterion provided here. For those with poor EHF thresholds, one approach could be to fit devices that extend the signal bandwidth. Devices such as the Earlens Photonic Transducer (Perkins et al. 2010) have been reported to significantly improve normal hearers’ ability to hear target speech in complex environments (Perkins et al. 2011; Levy et al. 2015; Struck & Prusick 2017). Several studies have demonstrated that extended bandwidth improved nonsense syllable and speech test results for normal hearers (Füllgrabe et al. 2010; Levy et al. 2015), who also prefer the sound quality these signals provide (Beck & Olsen 2008; Ricketts et al. 2008; Füllgrabe et al. 2010).
Another remediation approach, used alone or in combination with a device, could be to develop training packages that focus on working memory. To date there have been mixed research findings in relation to the efficacy of working memory training (Owen et al. 2010; Melby-Lervåg & Hulme 2013; Ferguson & Henshaw 2015; Ingvalson et al. 2015; Mackenzie 2015; Whitton et al. 2017), implying that further work is needed to develop training packages that cater to individual client needs and motivation levels; are sufficiently rewarding; and produce sustainable outcomes that withstand rigorous evaluation. Even if such evaluation reveals that working memory training provides only modest improvements in performance, offering it to clients may help to “legitimize” their concerns. This would be preferable to the status quo, which can leave clients questioning whether they are in fact experiencing an actual communication problem. In any case, it has been noted that even when significant improvements in post-training test scores only translate to small real-world effects, a client’s levels of confidence and self-efficacy may be significantly enhanced (Mackenzie 2015). For many clients, this may provide enough justification to undertake training.
The results of this investigation may have been affected by several factors, which should be taken into account when interpreting the findings. First, the procedure we used to segregate high and low performers meant that not everyone in the low CSS group was necessarily experiencing significant real-world listening difficulties. This may have influenced the results obtained; however, even considering this, the diagnostic criterion still yielded reasonable predictive accuracy. Second, rather than use a cross-validation procedure (which can overestimate model performance), it could be argued that it would be preferable to evaluate the efficacy of the diagnostic criterion by testing the procedure on a new population. Recruiting more participants was beyond the scope of this study; however, it is possible that existing data sets from other research groups could be used in this way.
The authors thank Jermy Pang for data collection and Mark Seeto for advice on statistical analysis and interpretation. I.Y. designed and performed experiments, analyzed data, and wrote the article; E.F.B. and M.S. designed experiments and provided interpretative analysis and critical revision. All authors discussed the results and implications and commented on the manuscript at all stages.
Abrams H. B., Kihm J. An introduction to MarkeTrak IX: A new baseline for the hearing aid market: MT9 reveals renewed encouragement as well as obstacles for consumers with hearing loss. The Hearing Review, 2015). 22, 16.
Badri R., Siegel J. H., Wright B. A. Auditory filter shapes and high-frequency hearing in adults who have impaired speech in noise
performance despite clinically normal audiograms. J Acoust Soc Am, 2011). 129, 852–863.
Beck D. L., Olsen J. Extended bandwidths in hearing aids. The Hearing Review, 2008). 15(11), 22–26.
Besser J., Koelewijn T., Zekveld A. A, et al. How linguistic closure and verbal working memory
relate to speech recognition in noise–a review. Trends Amplif, 2013). 17, 75–93.
Best V., Keidser G., Freeston K, et al. Evaluation of the NAL Dynamic Conversations Test in older listeners with hearing loss. Int J Audiol, 2018). 57, 221–229.
Bramhall N. F., Konrad-Martin D., McMillan G. P. Tinnitus and auditory perception after a history of noise exposure: Relationship to auditory brainstem response measures. Ear Hear. 2018). doi: 10.1097/AUD.0000000000000544.
Bramhall N. F., Konrad-Martin D., McMillan G. P, et al. Auditory brainstem response altered in humans with noise exposure despite normal outer hair cell function. Ear Hear, 2017). 38, e1–e12.
Bressler S., Goldberg H., Shinn-Cunningham B. Sensory coding and cognitive processing of sound in Veterans with blast exposure. Hear Res, 2017). 349, 98–110.
Cameron S., Glyde H., Dillon H. Listening in Spatialized Noise-Sentences Test (LiSN-S): Normative and retest reliability data for adolescents and adults up to 60 years of age. J Am Acad Audiol, 2011). 22, 697–709.
Chin T., Rickard N. S. The Music USE (MUSE) Questionnaire: An instrument to measure engagement in music. Music Perception, 2012). 29, 429–446.
Classon E., Rudner M., Rönnberg J. Working memory
compensates for hearing related phonological processing deficit. J Commun Disord, 2013). 46, 17–29.
da Rocha R. L., Atherino C. C., Frota S. M. High-frequency audiometry in normal hearing
military firemen exposed to noise. Braz J Otorhinolaryngol, 2010). 76, 687–694.
Daneman M., Carpenter P. A. Individual differences in working memory
and reading. J Verbal Learn Verbal Behav, 1980). 19, 450–466.
Dryden A., Allen H. A., Henshaw H, et al. The association between cognitive performance and speech-in-noise perception for adult listeners: A systematic literature review and meta-analysis. Trends Hear, 2017). 21, 2331216517744675.
Ferguson M. A., Henshaw H. Auditory training can improve working memory
, attention, and communication in adverse conditions for adults with hearing loss. Front Psychol, 2015). 6, 556.
Fulbright A. N. C., Le Prell C. G., Griffiths S. K, et al. Effects of recreational noise on threshold and suprathreshold measures of auditory function. Semin Hear, 2017). 38, 298–318.
Füllgrabe C., Rosen S. On the (un)importance of working memory
in speech-in-noise processing for listeners with normal hearing
thresholds. Front Psychol, 2016). 7, 1268.
Füllgrabe C., Baer T., Stone M. A, et al. Preliminary evaluation of a method for fitting hearing aids with extended bandwidth. Int J Audiol, 2010). 49, 741–753.
Gordon-Salant S., Cole S. S. Effects of age and working memory
capacity on speech recognition performance in noise among listeners with normal hearing
. Ear Hear, 2016). 37, 593–602.
Grinn S. K., Wiseman K. B., Baker J. A, et al. Hidden hearing loss? No effect of common recreational noise exposure on cochlear nerve response amplitude in humans. Front Neurosci, 2017). 11, 465.
Grose J. H., Buss E., Hall J. W. 3rd. Loud music exposure and cochlear synaptopathy
in young adults: Isolated auditory brainstem response effects but no perceptual consequences. Trends Hear, 2017). 21, 2331216517737417.
Guest H., Munro K. J., Prendergast G, et al. Tinnitus with a normal audiogram: Relation to noise exposure but no evidence for cochlear synaptopathy
. Hear Res, 2017). 344, 265–274.
Hanley J. A., McNeil B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1982). 143, 29–36.
Hastie T. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (2009). 2nd ed.). New York, NY: Springer.
Heinrich A., Henshaw H., Ferguson M. A. Only behavioral but not self-report measures of speech perception correlate with cognitive abilities. Front Psychol, 2016). 7, 576.
Hind S. E., Haines-Bazrafshan R., Benton C. L, et al. Prevalence of clinical referrals having hearing thresholds within normal limits. Int J Audiol, 2011). 50, 708–716.
Honaker J., King g., Blackwell M. Amelia II: A program for missing data. J Stat Software, 2011). 45, 1–47.
Ingvalson E. M., Dhar S., Wong P. C, et al. Working memory
training to improve speech perception in noise across languages. J Acoust Soc Am, 2015). 137, 3477–3486.
Kalikow D. N., Stevens K. N., Elliott L. L. Development of a test of speech intelligibility in noise using sentence materials with controlled word predictability. J Acoust Soc Am, 1977). 61, 1337–1351.
Kaufman A. S., Kaufman N. L. Kaufman Brief Intelligence Test Manual (2nd ed., pp. 5–20). 2004). Sydney, Australia: Pearson Australia Group Pty Ltd.
Keidser G., Best V., Freeston K, et al. Cognitive spare capacity: Evaluation data and its association with comprehension of dynamic conversations. Front Psychol, 2015). 6, 597.
Kuhn M., Johnson K. Applied Predictive Modeling. 2013). New York, NY: Springer.
Kujala T., Shtyrov Y., Winkler I, et al. Long-term exposure to noise impairs cortical sound processing and attention control. Psychophysiology, 2004). 41, 875–881.
Kujawa S. G., Liberman M. C. Adding insult to injury: cochlear nerve degeneration after “temporary” noise-induced hearing loss. J Neurosci, 2009). 29, 14077–14085.
Kumar P., Upadhyay P., Kumar A, et al. Extended high frequency audiometry in users of personal listening devices. Am J Otolaryngol Head Neck Med Surg, 2017). 38, 163–167.
Le Prell C. G., Spankovich C., Lobariñas E, et al. Extended high-frequency thresholds in college students: Effects of music player use and other recreational noise. J Am Acad Audiol, 2013). 24, 725–739.
Levy C. S., Freed J. D., Nilsson J. M, et al. Extended high-frequency bandwidth improves speech reception in the presence of spatially separated masking speech. Ear Hear, 2015). 36, e214–e224.
Liberman M. C., Epstein M. J., Cleveland S. S, et al. Toward a differential diagnosis of hidden hearing loss in humans. PLoS One, 2016). 11, e0162726.
Lunner T. Cognitive function in relation to hearing aid use. Int J Audiol, 2003). 42(Suppl 1), S49–S58.
Mackenzie D. Sound advice. New Scientist, 2015). 227, 36–38.
Marshall L., Lapsley Miller J. A., Guinan J. J, et al. Otoacoustic-emission-based medial-olivocochlear reflex assays for humans. J Acoust Soc Am, 2014). 136, 2697–2713.
Mehrparvar A. H., Mirmohammadi S. J., Ghoreyshi A, et al. High-frequency audiometry: A means for early diagnosis of noise-induced hearing loss. Noise Health, 2011). 13, 402–406.
Melby-Lervåg M., Hulme C. Is working memory
training effective? A meta-analytic review. Dev Psychol, 2013). 49, 270–291.
Mimosa Acoustics. (MOCR User Manual Help Version 1.0. 2014). Champaign, IL: Mimosa Acoustics, Inc.
Moore B. C., Sek A. Development of a fast method for determining sensitivity to temporal fine structure. Int J Audiol, 2009). 48, 161–171.
Moore B. C., Creeke S., Glasberg B. R, et al. A version of the TEN Test for use with ER-3A insert earphones. Ear Hear, 2012). 33, 554–557.
Moore D., Hunter L., Munro K. Benefits of extended high-frequency audiometry for everyone. Hear J, 2017). 70, 50–55.
Noble W., Jensen N. S., Naylor G, et al. A short form of the Speech, Spatial and Qualities of Hearing scale suitable for clinical use: The SSQ12. Int J Audiol, 2013). 52, 409–412.
Owen A. M., Hampshire A., Grahn J. A, et al. Putting brain training to the test. Nature, 2010). 465, 775–778.
Park S. H., Goo J. M., Jo C. H. Receiver operating characteristic (ROC) curve: Practical review for radiologists. Korean J Radiol, 2004). 5, 11–18.
Perkins R., Fay J. P., Rucker P, et al. The EarLens system: New sound transduction methods. Hear Res, 2010). 263, 104–113.
Perkins R. C., Fay J., Nilsson M. J, et al. The EarLens Photonic Transducer: Extended bandwidth. Otolaryngol Head Neck Surg, 2011). 145 (Suppl), P102.
Prendergast G., Guest H., Léger A, et al. Evidence that hidden hearing loss does not vary systematically as a function of noise exposure in young adults with normal audiometric hearing. J Acoust Soc Am, 2016). 139, 2122.
Prendergast G., Guest H., Munro K. J, et al. Effects of noise exposure on young adults with normal audiograms I: Electrophysiology. Hear Res, 2017). 344, 68–81.
Pryce H. The process of coping in King-Kopetzky Syndrome. Audiol Med, 2006). 4, 60–67.
Pryce H. King-Kopetzky syndrome? A bio-psychosocial approach to adult “APD”. Persp Hear Hear Disorders Res Diagn, 2015). 19, 22.
Pryce H., Hall A. The role of shared decision-making in audiologic rehabilitation. Persp Aural Rehab Instrument, 2014). 21, 15.
Pryce H., Wainwright D. Help-seeking for medically unexplained hearing difficulties: A qualitative study. Int J Ther Rehab, 2008). 15, 343–349.
R Core Team (R: A Language and Environment for Statistical Computing. 2016). Vienna, Austria: R Foundation for Statistical Computing.
Ricketts T. A., Dittberner A. B., Johnson E. E. High-frequency amplification and sound quality in listeners with normal through moderate hearing loss. J Speech Lang Hear Res, 2008). 51, 160–172.
Robertson I. H., Ward T., Ridgeway V, et al. The Test of Everday Attention Manual. 1994). London, United Kingdom: Pearson Assessment.
Robin X., Turck N., Hainard A, et al. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011). 12, 77.
Rodríguez Valiente A., Roldán Fidalgo A., Villarreal I. M, et al. Extended high-frequency audiometry (9000–20 000 Hz). Usefulness in audiological diagnosis. Acta Otorrinolaringol (English Edition), 2016). 67, 40–44.
Rubin D. B. Multiple Imputation for Nonresponse in Surveys. 1987). Canada: John Wiley & Sons, Inc.
Rudner M., Lunner T. Cognitive spare capacity and speech communication: A narrative overview. Biomed Res Int, 2014). 2014, 869726.
Rudner M., Rönnberg J., Lunner T. Working memory
supports listening in noise for persons with hearing impairment. J Am Acad Audiol, 2011). 22, 156–167.
Ruggles D., Bharadwaj H., Shinn-Cunningham B. G. Why middle-aged listeners have trouble hearing in everyday settings. Curr Biol, 2012). 22, 1417–1422.
Saunders G. H., Haggard M. P. The clinical assessment of obscure auditory dysfunction–1. Auditory and psychological factors. Ear Hear, 1989). 10, 200–208.
Saunders G. H., Haggard M. P. The clinical assessment of “Obscure Auditory Dysfunction” (OAD) 2. Case control analysis of determining factors. Ear Hear, 1992). 13, 241–254.
Schoof T., Rosen S. The role of auditory and cognitive factors in understanding speech in noise
by normal-hearing older listeners. Front Aging Neurosci, 2014). 6, 307.
Smith L. S., Pichora-Fuller K. M., Alexander K. G. Development of the word auditory recognition and recall measure: A working memory
test for use in rehabilitative audiology. Ear Hear, 2016). 37, e360–e376.
Somma G., Pietroiusti A., Magrini A, et al. Extended high-frequency audiometry and noise induced hearing loss in cement workers. Am J Ind Med, 2008). 51, 452–462.
Spankovich C., Gonzalez V. B., Su D, et al. Self reported hearing difficulty, tinnitus, and normal audiometric thresholds, the National Health and Nutrition Examination Survey 1999–2002. Hear Res, 2018). 358, 30–36.
Stamper G. C., Johnson T. A. Auditory function in normal-hearing, noise-exposed human ears. Ear Hear, 2015). 36, 172–184.
Stenbäck V., Hällgren M., Larsby B. Executive functions and working memory
capacity in speech communication under adverse conditions. Speech Lang Hear, 2016). 19, 218–226.
Stephens D., Zhao F., Kennedy V. Is there an association between noise exposure and King Kopetzky Syndrome? Noise Health, 2003). 5, 55–62.
Struck C. J., Prusick L. Comparison of real-world bandwidth in hearing aids vs earlens light-driven hearing aid system. The Hearing Review, 2017). 24, 24.
Sulaiman A. H., Husain R., Seluakumaran K. Evaluation of early hearing damage in personal listening device users using extended high-frequency audiometry and otoacoustic emissions. Eur Arch Otorhinolaryngol, 2014). 271, 1463–1470.
Swets J. A. Measuring the accuracy of diagnostic systems. Science, 1988). 240, 1285–1293.
Tremblay K. L., Pinto A., Fischer M. E, et al. Self-reported hearing difficulties among adults with normal audiograms: The Beaver Dam Offspring Study. Ear Hear, 2015). 36, e290–e299.
Valderrama J. T., Beach E. F., Yeend I, et al. Effects of lifetime noise exposure on the middle-age human auditory brainstem response, tinnitus and speech-in-noise intelligibility. Hear Res, 2018). 365, 36–48.
Whitton J. P., Hancock K. E., Shannon J. M, et al. Audiomotor perceptual training enhances speech intelligibility in background noise. Curr Biol, 2017). 27, 3237–3247.e6.
Yeend I., Beach E. F., Sharma M, et al. The effects of noise exposure and musical training on suprathreshold auditory processing and speech perception in noise. Hear Res, 2017). 353, 224–236.
Zekveld A. A. Ten years of measuring Text Reception Thresholds: What are we actually measuring? 2017). In Fourth International Conference on Cognitive Hearing Science for Communication. Linkoping, Sweden.
Zekveld A. A., George E. L., Kramer S. E, et al. The development of the text reception threshold test: A visual analogue of the speech reception threshold test. J Speech Lang Hear Res, 2007). 50, 576–584.
Zhao F., Stephens D., Pryce H, et al. Rehabilitative management strategies in patients with King-Kopetzky Syndrome. Aus N Z J Audiol, 2008). 30, 119–127.