Hearing loss is one of the most prevalent chronic conditions, affecting 10% of the U.S. population. Although signal amplification by modern hearing aids makes sound more audible to hearing impaired listeners, speech understanding in background noise remains one of the biggest challenges in hearing prosthesis. The proposed research seeks a solution to this challenge by developing a speech segregation system that can significantly improve intelligibility of noisy speech for listeners with hearing loss, with the loner term goal of applying to hearing aid design. Unlike traditional speech enhancement and beam forming algorithms, the proposed monaural (one-microphone) solution will be grounded in perceptual principles of auditory scene analysis. There are two stages in auditory scene analysis: A simultaneous organization stage that groups concurrent sound components and a sequential organization stage that groups sound components across time. This project is designed to achieve three specific aims.
The first aim i s to improve word recognition scores of hearing-impaired listeners in background noise. The second and the third aims are to improve the sentence-level intelligibility scores in background noise and in interfering speech, respectively. To achieve the first aim, a simultaneous organization algorithm will be developed that uses the pitch cue to segregate voiced speech and the onset and offset cues to segregate unvoiced speech. To achieve aims 2 and 3, a sequential organization algorithm will be developed that groups simultaneously organized streams across time to produce a sentence segregated from background interference. Sequential organization will be performed by analyzing pitch characteristics and a novel clustering method on the basis of incremental speaker modeling. A set of seven speech intelligibility experiments involving both hearing-impaired and normal-hearing listeners will be conducted to systematically evaluate the developed system.

Public Health Relevance

A widely acknowledged deficit of hearing loss is reduced intelligibility of noisy speech. How to improve speech intelligibility of hearing impaired listener in noisy environments is a major challenge. This project will directly address this challenge and the results from the project are expected to yield technical solutions to better hearing aid design, potentially benefiting millions of individuals who suffer from hearing loss.

National Institute of Health (NIH)
National Institute on Deafness and Other Communication Disorders (NIDCD)
Research Project (R01)
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Auditory System Study Section (AUD)
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Miller, Roger
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Ohio State University
Biostatistics & Other Math Sci
Schools of Engineering
United States
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Zhao, Yan; Wang, DeLiang; Johnson, Eric M et al. (2018) A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions. J Acoust Soc Am 144:1627
Healy, Eric W; Delfarah, Masood; Vasko, Jordan L et al. (2017) An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker. J Acoust Soc Am 141:4230
Wang, DeLiang (2017) Deep Learning Reinvents the Hearing Aid: Finally, wearers of hearing aids can pick out a voice in a crowded room. IEEE Spectr 54:32-37
Chen, Jitong; Wang, DeLiang (2017) Long short-term memory for speaker generalization in supervised speech separation. J Acoust Soc Am 141:4705
Liu, Yuzhou; Wang, DeLiang (2017) Speaker-dependent multipitch tracking using deep neural networks. J Acoust Soc Am 141:710
Zhang, Xueliang; Wang, DeLiang (2017) Deep Learning Based Binaural Speech Separation in Reverberant Environments. IEEE/ACM Trans Audio Speech Lang Process 25:1075-1084
Williamson, Donald S; Wang, Yuxuan; Wang, DeLiang (2016) Complex Ratio Masking for Monaural Speech Separation. IEEE/ACM Trans Audio Speech Lang Process 24:483-492
Chen, Jitong; Wang, Yuxuan; Wang, DeLiang (2016) Noise Perturbation for Supervised Speech Separation. Speech Commun 78:1-10
Zhang, Xiao-Lei; Wang, DeLiang (2016) A Deep Ensemble Learning Method for Monaural Speech Separation. IEEE/ACM Trans Audio Speech Lang Process 24:967-977
Chen, Jitong; Wang, Yuxuan; Yoho, Sarah E et al. (2016) Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises. J Acoust Soc Am 139:2604

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