Eric W Healy
Overview
Explore the profile of Eric W Healy including associated specialties, affiliations and a list of published articles.
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Articles
57
Citations
549
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Recent Articles
1.
Johnson E, Healy E
J Acoust Soc Am
. 2024 Dec;
156(6):3958-3969.
PMID: 39666959
Hearing impairment is often characterized by poor speech-in-noise recognition. State-of-the-art laboratory-based noise-reduction technology can eliminate background sounds from a corrupted speech signal and improve intelligibility, but it can also hinder...
2.
Johnson E, Healy E
Ear Hear
. 2024 May;
45(6):1444-1460.
PMID: 38816900
Objectives: This study aimed to determine the speech-to-background ratios (SBRs) at which normal-hearing (NH) and hearing-impaired (HI) listeners can recognize both speech and environmental sounds when the two types of...
3.
Healy E, Johnson E, Pandey A, Wang D
J Acoust Soc Am
. 2023 May;
153(5):2751.
PMID: 37133814
Recent years have brought considerable advances to our ability to increase intelligibility through deep-learning-based noise reduction, especially for hearing-impaired (HI) listeners. In this study, intelligibility improvements resulting from a current...
4.
Borrie S, Yoho S, Healy E, Barrett T
J Speech Lang Hear Res
. 2023 Mar;
66(5):1853-1866.
PMID: 36944186
Purpose: Background noise reduces speech intelligibility. Time-frequency (T-F) masking is an established signal processing technique that improves intelligibility of neurotypical speech in background noise. Here, we investigated a novel application...
5.
Carter B, Apoux F, Healy E
J Speech Lang Hear Res
. 2022 Aug;
65(9):3548-3565.
PMID: 35973100
Purpose: A dual-task paradigm was implemented to investigate how noise type and sentence context may interact with age and hearing loss to impact word recall during speech recognition. Method: Three...
6.
Healy E, Taherian H, Johnson E, Wang D
J Acoust Soc Am
. 2021 Dec;
150(5):3976.
PMID: 34852625
The fundamental requirement for real-time operation of a speech-processing algorithm is causality-that it operate without utilizing future time frames. In the present study, the performance of a fully causal deep...
7.
Healy E, Johnson E, Delfarah M, Krishnagiri D, Sevich V, Taherian H, et al.
J Acoust Soc Am
. 2021 Oct;
150(4):2526.
PMID: 34717521
The practical efficacy of deep learning based speaker separation and/or dereverberation hinges on its ability to generalize to conditions not employed during neural network training. The current study was designed...
8.
Healy E, Tan K, Johnson E, Wang D
J Acoust Soc Am
. 2021 Jul;
149(6):3943.
PMID: 34241481
Real-time operation is critical for noise reduction in hearing technology. The essential requirement of real-time operation is causality-that an algorithm does not use future time-frame information and, instead, completes its...
9.
Fogerty D, Sevich V, Healy E
J Acoust Soc Am
. 2020 Oct;
148(3):1552.
PMID: 33003879
Adverse listening conditions involve glimpses of spectro-temporal speech information. This study investigated if the acoustic organization of the spectro-temporal masking pattern affects speech glimpsing in "checkerboard" noise. The regularity and...
10.
Healy E, Johnson E, Delfarah M, Wang D
J Acoust Soc Am
. 2020 Jul;
147(6):4106.
PMID: 32611178
Deep learning based speech separation or noise reduction needs to generalize to voices not encountered during training and to operate under multiple corruptions. The current study provides such a demonstration...