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An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker.
J Acoust Soc Am. 2017 06; 141(6):4230.JA

Abstract

Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise. A machine learning algorithm is introduced here to address this listening situation. A deep neural network was trained to estimate the ideal ratio mask for a male target talker in the presence of a female competing talker. The monaural algorithm was found to produce sentence-intelligibility increases for hearing-impaired (HI) and normal-hearing (NH) listeners at various signal-to-noise ratios (SNRs). This benefit was largest for the HI listeners and averaged 59%-points at the least-favorable SNR, with a maximum of 87%-points. The mean intelligibility achieved by the HI listeners using the algorithm was equivalent to that of young NH listeners without processing, under conditions of identical interference. Possible reasons for the limited ability of HI listeners to perceptually segregate concurrent voices are reviewed as are possible implementation considerations for algorithms like the current one.

Authors+Show Affiliations

Department of Speech and Hearing Science, The Ohio State University, Columbus, Ohio 43210, USA.Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.Department of Speech and Hearing Science, The Ohio State University, Columbus, Ohio 43210, USA.Department of Speech and Hearing Science, The Ohio State University, Columbus, Ohio 43210, USA.Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

28618817

Citation

Healy, Eric W., et al. "An Algorithm to Increase Intelligibility for Hearing-impaired Listeners in the Presence of a Competing Talker." The Journal of the Acoustical Society of America, vol. 141, no. 6, 2017, p. 4230.
Healy EW, Delfarah M, Vasko JL, et al. An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker. J Acoust Soc Am. 2017;141(6):4230.
Healy, E. W., Delfarah, M., Vasko, J. L., Carter, B. L., & Wang, D. (2017). An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker. The Journal of the Acoustical Society of America, 141(6), 4230. https://doi.org/10.1121/1.4984271
Healy EW, et al. An Algorithm to Increase Intelligibility for Hearing-impaired Listeners in the Presence of a Competing Talker. J Acoust Soc Am. 2017;141(6):4230. PubMed PMID: 28618817.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker. AU - Healy,Eric W, AU - Delfarah,Masood, AU - Vasko,Jordan L, AU - Carter,Brittney L, AU - Wang,DeLiang, PY - 2017/6/17/entrez PY - 2017/6/18/pubmed PY - 2019/7/10/medline SP - 4230 EP - 4230 JF - The Journal of the Acoustical Society of America JO - J Acoust Soc Am VL - 141 IS - 6 N2 - Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise. A machine learning algorithm is introduced here to address this listening situation. A deep neural network was trained to estimate the ideal ratio mask for a male target talker in the presence of a female competing talker. The monaural algorithm was found to produce sentence-intelligibility increases for hearing-impaired (HI) and normal-hearing (NH) listeners at various signal-to-noise ratios (SNRs). This benefit was largest for the HI listeners and averaged 59%-points at the least-favorable SNR, with a maximum of 87%-points. The mean intelligibility achieved by the HI listeners using the algorithm was equivalent to that of young NH listeners without processing, under conditions of identical interference. Possible reasons for the limited ability of HI listeners to perceptually segregate concurrent voices are reviewed as are possible implementation considerations for algorithms like the current one. SN - 1520-8524 UR - https://www.unboundmedicine.com/medline/citation/28618817/An_algorithm_to_increase_intelligibility_for_hearing_impaired_listeners_in_the_presence_of_a_competing_talker_ L2 - https://doi.org/10.1121/1.4984271 DB - PRIME DP - Unbound Medicine ER -