ArticleThe Journal of the Acoustical Society of America2019
A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.
Article in The Journal of the Acoustical Society of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Impact of The Menstrual Cycle on Physical Recovery after Small-Sided Games: A Crossover Study in Women's Soccer Players.Journal of sports science & medicine · 2025Trial
- Acoustic Scene-Aware Processing and Auditory Model-Based Compensation Strategies.Journal of the Association for Research in Otolaryngology : JARO · 2026Review
- Perspectives of audiologists in China on artificial intelligence in clinical practice and professional identity: a qualitative study.BMC medical education · 2026Article
- An ideal compressed mask for increasing speech intelligibility without sacrificing environmental sound recognitiona).The Journal of the Acoustical Society of America · 2024Article
- Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users.Scientific reports · 2024Article
- 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
- Parameter tuning of time-frequency masking algorithms for reverberant artifact removal within the cochlear implant stimulus.Cochlear implants international · 2022Article
- Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.Journal of the Association for Research in Otolaryngology : JARO · 2022Review
- 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
- Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management.Insects · 2021Article
- Real-time single-channel deep neural network-based speech enhancement on edge devices.Interspeech · 2020Article
- A two-stage deep learning algorithm for talker-independent speaker separation in reverberant conditions.The Journal of the Acoustical Society of America · 2020Article
- Efficient two-microphone speech enhancement using basic recurrent neural network cell for hearing and hearing aids.The Journal of the Acoustical Society of America · 2020Article
- A talker-independent deep learning algorithm to increase intelligibility for hearing-impaired listeners in reverberant competing talker conditions.The Journal of the Acoustical Society of America · 2020Article
- Adoption of Human Personality Development Theory Combined With Deep Neural Network in Entrepreneurship Education of College Students.Frontiers in psychology · 2020Article
- Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants.The Journal of the Acoustical Society of America · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
For deep learning based speech segregation to have translational significance as a noise-reduction tool, it must perform in a wide variety of acoustic environments. In the current study, performance was examined when target speech was subjected to interference from a single talker and room reverberation. Conditions were compared in which an algorithm was trained to remove both reverberation and interfering speech, or only interfering speech. A recurrent neural network incorporating bidirectional long short-term memory was trained to estimate the ideal ratio mask corresponding to target speech. Substantial intelligibility improvements were found for hearing-impaired (HI) and normal-hearing (NH) listeners across a range of target-to-interferer ratios (TIRs). HI listeners performed better with reverberation removed, whereas NH listeners demonstrated no difference. Algorithm benefit averaged 56 percentage points for the HI listeners at the least-favorable TIR, allowing these listeners to perform numerically better than young NH listeners without processing. The current study highlights the difficulty associated with perceiving speech in reverberant-noisy environments, and it extends the range of environments in which deep learning based speech segregation can be effectively applied. This increasingly wide array of environments includes not only a variety of background noises and interfering speech, but also room reverberation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.