Eric W. Healy
Neuroscience · The Ohio State University
Publications
126
Citations
2,434
Est. group size
~3
Recurring co-author estimate
Active years
35
Publishing since 1992
Eric W. Healy's research focuses on how people understand speech in difficult listening conditions, such as background noise, and how deep-learning algorithms can be designed to make speech clearer for listeners with hearing loss. His work spans speech intelligibility, noise reduction, environmental sound recognition, and speech affected by conditions like dysarthria (a motor speech disorder). Much of the recent work combines behavioral listening experiments with computational/deep-learning models for speech enhancement.
Output was higher around 2017-2018 (8-9 papers/year) but has settled into a steadier, lower rate of roughly 2-4 publications per year over the past five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Background noise inhibits listeners' use of contextual cues for dysarthric speech
JASA Express Letters · 2026
- Estimating band importance for environmental sound recognition using deep learning
The Journal of the Acoustical Society of America · 2026
- Combined generative and predictive modeling for speech super-resolution
Computer Speech & Language · 2025
- Perceptual effects of reducing algorithmic latency on deep-learning based noise reduction
The Journal of the Acoustical Society of America · 2025
- Communication in Complex Situations: The Combined Influence of Dysarthria and Sensorineural Hearing Loss on Speech Perception in Everyday Noisy Environments
Journal of Speech Language and Hearing Research · 2025
- The Optimal Speech-to-Background Ratio for Balancing Speech Recognition With Environmental Sound Recognition
Ear and Hearing · 2024
- An ideal compressed mask for increasing speech intelligibility without sacrificing environmental sound recognition
The Journal of the Acoustical Society of America · 2024
- Combined Generative and Predictive Modeling for Speech Super-resolution
arXiv (Cornell University) · 2024
- Progress made in the efficacy and viability of deep-learning-based noise reduction
The Journal of the Acoustical Society of America · 2023
- The Application of Time–Frequency Masking To Improve Intelligibility of Dysarthric Speech in Background Noise
Journal of Speech Language and Hearing Research · 2023
- Maximizing environmental sound recognition and speech intelligibility using time-frequency masking
The Journal of the Acoustical Society of America · 2023
- Improving intelligibility of dysarthric speech in noise for listeners with hearing loss
The Journal of the Acoustical Society of America · 2023
- The Influence of Noise Type and Semantic Predictability on Word Recall in Older Listeners and Listeners With Hearing Impairment
Journal of Speech Language and Hearing Research · 2022
- An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners
The Journal of the Acoustical Society of America · 2021
- Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility
The Journal of the Acoustical Society of America · 2021
- The Journal of the Acoustical Society of America×34
- Journal of Speech Language and Hearing Research×6
- Ear and Hearing×1
- Computer Speech & Language×1
- The Hearing Journal×1
- Sarah E. Yoho
Neuroscience · The Ohio State University
- Agudemu Borjigin
Neuroscience · Purdue University West Lafayette
- Jennifer J. Lentz
Neuroscience · Indiana University
- Joshua M. Alexander
Neuroscience · Purdue University West Lafayette
- Donghyeon Yun
Neuroscience · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
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