# Improving intelligibility in noise for hearing-impaired listeners

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2020 · $313,376

## Abstract

Project Summary
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...

## Key facts

- **NIH application ID:** 9998675
- **Project number:** 5R01DC015521-05
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** ERIC W HEALY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $313,376
- **Award type:** 5
- **Project period:** 2016-09-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9998675

## Citation

> US National Institutes of Health, RePORTER application 9998675, Improving intelligibility in noise for hearing-impaired listeners (5R01DC015521-05). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9998675. Licensed CC0.

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