ArticleThe Journal of the Acoustical Society of America2020
A talker-independent deep learning algorithm to increase intelligibility for hearing-impaired listeners in reverberant competing talker conditions.
Article in The Journal of the Acoustical Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- An ideal compressed mask for increasing speech intelligibility without sacrificing environmental sound recognitiona).The Journal of the Acoustical Society of America · 2024Article
- The Application of Time-Frequency Masking To Improve Intelligibility of Dysarthric Speech in Background Noise.Journal of speech, language, and hearing research : JSLHR · 2023Article
- Progress made in the efficacy and viability of deep-learning-based noise reduction.The Journal of the Acoustical Society of America · 2023Article
- Suppressing reverberation in cochlear implant stimulus patterns using time-frequency masks based on phoneme groups.Proceedings of meetings on acoustics. Acoustical Society of America · 2022Article
- A causal and talker-independent speaker separation/dereverberation deep learning algorithm: Cost associated with conversion to real-time capable operation.The Journal of the Acoustical Society of America · 2021Article
- Deep learning based speaker separation and dereverberation can generalize across different languages to improve intelligibility.The Journal of the Acoustical Society of America · 2021Article
- An effectively causal deep learning algorithm to increase intelligibility in untrained noises for hearing-impaired listeners.The Journal of the Acoustical Society of America · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
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 for hearing-impaired (HI) listeners. Sentence intelligibility was assessed under conditions of a single interfering talker and substantial amounts of room reverberation. A talker-independent deep computational auditory scene analysis (CASA) algorithm was employed, in which talkers were separated and dereverberated in each time frame (simultaneous grouping stage), then the separated frames were organized to form two streams (sequential grouping stage). The deep neural networks consisted of specialized convolutional neural networks, one based on U-Net and the other a temporal convolutional network. It was found that every HI (and normal-hearing, NH) listener received algorithm benefit in every condition. Benefit averaged across all conditions ranged from 52 to 76 percentage points for individual HI listeners and averaged 65 points. Further, processed HI intelligibility significantly exceeded unprocessed NH intelligibility. Although the current utterance-based model was not implemented as a real-time system, a perspective on this important issue is provided. It is concluded that deep CASA represents a powerful framework capable of producing large increases in HI intelligibility for potentially any two voices.
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