# Speech segregation to improve intelligility of reverberant-noisy speech

> **NIH NIH R01** · OHIO STATE UNIVERSITY · 2022 · $300,366

## Abstract

Project Summary
Hearing loss is one of the most prevalent chronic conditions, affecting 37.5 million Americans. Although
signal amplification in modern hearing aids makes sound more audible to hearing impaired listeners,
speech understanding in background interference remains the biggest challenge by hearing aid wearers.
The proposed research seeks a monaural (one-microphone) solution to this challenge by developing
supervised speech segregation based on deep learning. Unlike traditional speech enhancement, deep
learning based speech segregation is driven by training data, and three components of a deep neural
network (DNN) model are features, training targets, and network architectures. Recently, deep learning has
achieved tremendous successes in a variety of real world applications. Our approach builds on the progress
made in the PI's previous R01 project which demonstrated, for the first time, substantial speech intelligibility
improvements for hearing-impaired listeners in noise. A main focus of the proposed work in this cycle is to
combat room reverberation in addition to background interference. The proposed work is designed to
achieve three specific aims. The first aim is to improve intelligibility of reverberant-noisy speech for hearing-
impaired listeners. To achieve this aim, we will train DNNs to perform time-frequency masking. The second
aim is to improve intelligibility of reverberant speech in the presence of competing speech. To achieve this
aim, we will perform DNN training to estimate two ideal masks, one for the target talker and the other for the
interfering talker. The third aim is to improve intelligibility of reverberant speech in combined speech and
nonspeech interference. To achieve this aim, we will develop a two-stage DNN model where the first stage
will be trained to remove nonspeech interference and the second stage to remove interfering speech. Eight
speech intelligibility experiments involving both hearing-impaired and normal-hearing listeners will be
conducted to systematically evaluate the developed system. The proposed project is expected to
substantially close the speech intelligibility gap between hearing-impaired and normal-hearing listeners in
daily conditions, with the ultimate goal of removing the gap altogether.

## Key facts

- **NIH application ID:** 10318523
- **Project number:** 5R01DC012048-10
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Donald Williamson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $300,366
- **Award type:** 5
- **Project period:** 2013-01-01 → 2024-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10318523

## Citation

> US National Institutes of Health, RePORTER application 10318523, Speech segregation to improve intelligility of reverberant-noisy speech (5R01DC012048-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10318523. Licensed CC0.

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