# Real-time deep learning to improve speech intelligibility in noise

> **NIH NIH F32** · OHIO STATE UNIVERSITY · 2022 · $2,500

## Abstract

Project Summary/Abstract
 One in eight Americans has hearing loss, and this constitutes a major health and economic burden
(Blackwell et al., 2014). The primary complaint of hearing-impaired (HI) listeners is difficulty understanding
speech when background noise is present (see Dillon, 2012). While hearing aids (HAs) have improved in
recent years, they still provide little benefit in noisy environments. For decades, a means of improving the
ability to understand speech in background noise appeared unattainable, despite substantial amounts of
research by both universities and HA companies. This changed when deep learning provided the first
demonstration of a single-microphone algorithm that improves intelligibly in noise for HI listeners (Healy et al.,
2013, 2014, 2015). Although this algorithm provides massive intelligibility improvements (even allowing
listeners to improve intelligibility from floor to ceiling levels), it is currently not implemented to operate in real
time and is therefore not suitable for implementation into HAs and cochlear implants (CIs). What is needed,
therefore, is a highly effective noise-reduction algorithm that is capable of operating in real time. This project
aims to address this critical need.
 The long-term goal of the currently proposed project is to alleviate HI listeners’ predominant hearing
handicap, which is difficulty understanding speech in background noise. The first aim introduces a new
algorithm, based on a novel foundational scheme, that is designed to provide substantial benefit for any HI
listener in real time. This algorithm will be well suited for implementation into HAs, CIs, and other face-to-face
communication applications. The effectiveness of this new algorithm will be quantified using both HI and
normal-hearing (NH) listeners. The second aim expands upon this new algorithm by modifying it to accept a
small amount of future time-frame information, which could improve its noise-reduction performance but will
introduce a brief processing delay. The rationale is that different devices have different allowable latencies.
Face-to-face communication devices (HAs, CIs, etc.) have strict low-latency requirements, but other important
communication systems (e.g., telephones) have different requirements. It is possible that the addition of future
time-frame information within these requirements (up to 150 ms) will result in even better speech intelligibility.
But the magnitude of any potential benefit is unknown. This critical information will be established currently.
Using both HI and NH listeners, we will measure intelligibility for noisy sentences that have been processed
using various amounts of future time information.
 This comprehensive fellowship training plan will provide individualized, mentored research training from
world-class faculty in a highly supportive and productive environment. The proposed work will endow the
applicant with the skills needed to transition to the next stage of his rese...

## Key facts

- **NIH application ID:** 10558196
- **Project number:** 3F32DC019314-02S1
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Eric Martin Johnson
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $2,500
- **Award type:** 3
- **Project period:** 2022-02-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10558196

## Citation

> US National Institutes of Health, RePORTER application 10558196, Real-time deep learning to improve speech intelligibility in noise (3F32DC019314-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10558196. Licensed CC0.

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