# Individualized Assessment and Prediction of Speech-Recognition Performance In Adults with Age-related Hearing Loss

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2020 · $299,598

## Abstract

The main complaint from listeners with age-related hearing loss is the difficulty in understanding speech in
noisy environments. The sources of the speech-understanding difficulty involve auditory and cognitive factors
and vary from one listener to another. Developing models of speech intelligibility that can account for these
factors is necessary for predicting expected speech-recognition performance with or without the use of a
hearing aid. Moreover, if such models can be efficiently fitted to individual hearing-aid users, then the
amplification profile in the hearing aid can be customized to the users' specific needs. However, such efficient
diagnostic procedures for fitting models of speech-intelligibility are not yet available. The proposed research
program will address this issue directly. The long-term goal of the program is to establish an efficient diagnostic
test to enable individualized hearing-aid fitting. As a first step toward this goal, a Bayesian adaptive procedure
for fitting a widely-adopted model of speech intelligibility, i.e. the Speech Intelligibility Index (ANSI S3.5-1997),
to individual listeners will be examined in detail. The Bayesian adaptive procedure uses a speech recognition
task, similar to clinical speech audiometry, and it allows the estimation of the model parameters for the Speech
Intelligibility Index using as few as 75 test sentences (approximately 12 minutes of testing time). These
estimated parameters indicate (1) how much acoustic cues in various frequency bands are being used for
speech recognition, (2) the signal-to-noise ratio required to reach a performance level of 50% correct
recognition, and (3) the listener's benefits from contextual cues in speech. The relationship between these
model parameters to listener's auditory and cognitive skills will be systematically evaluated using a group of
older adults with diverse age and hearing status. The parameters will also be studied under two common
listening conditions: speech recognition in temporally fluctuating backgrounds, and speech recognition with
visual cues (i.e. the display of the talker's face). The dependencies of the model parameters for these
commonly occurring listening conditions will be investigated. Additionally, the estimated model using the
Bayesian adaptive procedure will be used to predict speech-recognition performance under aided and unaided
conditions. Whether the individualized Speech Intelligibility Index provides additional predictive power
compared to the standard model will be evaluated. The estimated model will also be used to optimize the
amplification profiles for individual hearing-impaired listeners, and its relationship to the listeners' preferred
amplification profiles will be examined. Upon the completion of the proposed research program, a model will be
established to provide comprehensive profiling of listeners' speech-recognition performance. Moreover, a set
of tools will be made available to efficiently fit the model...

## Key facts

- **NIH application ID:** 10221416
- **Project number:** 7R01DC017988-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Yi Shen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $299,598
- **Award type:** 7
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10221416

## Citation

> US National Institutes of Health, RePORTER application 10221416, Individualized Assessment and Prediction of Speech-Recognition Performance In Adults with Age-related Hearing Loss (7R01DC017988-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10221416. Licensed CC0.

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