The primary complaint of hearing-impaired (HI) listeners is poor speech understanding when background noise is present (see Dillon, 2012). This problem can therefore be considered the most significant for the estimated 37.5 million Americans with hearing loss (NIDCD, 2015). Accordingly, a solution to this problem has commonly been considered a ?holy grail? of our field. One proposed solution involves a single-microphone algorithm to extract speech from background noise. This may be considered an ultimate goal, because it is the algorithm that performs the task that the listener cannot. But despite 50 years of effort by groups around the world, an algorithm capable of improving intelligibility, especially for HI listeners, has remained elusive. We have recently provided the first demonstration of an algorithm capable of improving intelligibly in noise for HI listeners (Healy et al., 2013b, 2014, 2015). Not only is this work seminal, but the intelligibility improvements are substantial. Prior to algorithm processing, most of our HI listeners were able to understand roughly 1 in every 3 words within noisy sentences, and some scores were as low as 0-10%. Following algorithm processing, intelligibility for many of our HI listeners improved to roughly 90%. The long-term goal of the currently proposed study is to advance our ability to remedy the speech-in-noise problem for HI listeners.
The first aim establishes basic information essential to our understanding of speech recognition in noise. During this aim, we establish what we have termed ?noise susceptibility? for each individual frequency region of speech. We argue here that current efforts confound noise susceptibility with speech band importance, so that noise susceptibility is not known. We then provide direct and immediate application of this knowledge through a correction factor to incorporate noise susceptibility into the Speech Intelligibility Index (ANSI, 1997). During the second and third aims, we provide translational significance by advancing our algorithm in fundamental and important ways. During Aim 2, we establish a novel advancement that maximizes speech information while minimizing noise. We accomplish this by incorporating our understanding of noise susceptibility and speech band importance into our algorithm. During Aim 3, we compare the intelligibility and sound quality resulting from three different foundational schemes for our algorithm. One scheme is novel and will be introduced here. It promises to offer the advantages of both schemes we have already implemented. Overall, the current study has the potential to transform our basic understanding of speech recognition in noise and improve the ANSI standard used to predict it. Further, the proposed study is translational and addresses the primary limitation of HI listeners. We address this highly significant issue by advancing our algorithm in important and fundamental ways, thus progressing closer to our ultimate goal of implementation into hearing aids and cochlear implants. The contributions described here have the potential to substantially impact quality of life for millions of Americans and transform our treatment of hearing loss.
An estimated 37.5 million Americans have hearing loss, which commonly leads to difficulty understanding speech in background noise. The proposed study will further our understanding of how listeners process speech and improve our treatment of hearing loss.
| Fogerty, Daniel; Carter, Brittney L; Healy, Eric W (2018) Glimpsing speech in temporally and spectro-temporally modulated noise. J Acoust Soc Am 143:3047 |
| Healy, Eric W; Vasko, Jordan L (2018) An ideal quantized mask to increase intelligibility and quality of speech in noise. J Acoust Soc Am 144:1392 |
| Yoho, Sarah E; Apoux, Frédéric; Healy, Eric W (2018) The noise susceptibility of various speech bands. J Acoust Soc Am 143:2527 |
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| Yoho, Sarah E; Healy, Eric W; Youngdahl, Carla L et al. (2018) Speech-material and talker effects in speech band importance. J Acoust Soc Am 143:1417 |
| Zhao, Yan; Wang, DeLiang; Johnson, Eric M et al. (2018) A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions. J Acoust Soc Am 144:1627 |
| Blackett, Deena Schwen; Harnish, Stacy M; Lundine, Jennifer P et al. (2017) The Effect of Stimulus Valence on Lexical Retrieval in Younger and Older Adults. J Speech Lang Hear Res 60:2081-2089 |
| Healy, Eric W; Delfarah, Masood; Vasko, Jordan L et al. (2017) An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker. J Acoust Soc Am 141:4230 |
