ArticleThe Journal of the Acoustical Society of America2015
An algorithm to increase speech intelligibility for hearing-impaired listeners in novel segments of the same noise type.
Article in The Journal of the Acoustical Society of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed, 95 citations in OpenAlex.
- Communication in Complex Situations: The Combined Influence of Dysarthria and Sensorineural Hearing Loss on Speech Perception in Everyday Noisy Environments.Journal of speech, language, and hearing research : JSLHR · 2025Article
- Personalizing Cochlear Implant Care in Single-Sided Deafness: A Distinct Paradigm from Bilateral Hearing Loss.Journal of personalized medicine · 2025Review
- 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
- The Influence of Noise Type and Semantic Predictability on Word Recall in Older Listeners and Listeners With Hearing Impairment.Journal of speech, language, and hearing research : JSLHR · 2022Article
- Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical auditory responses.Frontiers in neuroscience · 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
- Creating Clarity in Noisy Environments by Using Deep Learning in Hearing Aids.Seminars in hearing · 2021Review
- 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
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- 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
- 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
- Assistive Hearing Technology for Deaf and Hard-of-Hearing Spoken Language Learners.Education sciences · 2019Article
- The optimal threshold for removing noise from speech is similar across normal and impaired hearing-a time-frequency masking study.The Journal of the Acoustical Society of America · 2019Article
- A deep learning algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker and reverberation.The Journal of the Acoustical Society of America · 2019Article
- A Real-Time Convolutional Neural Network Based Speech Enhancement for Hearing Impaired Listeners Using Smartphone.IEEE access : practical innovations, open solutions · 2019Article
- Supervised Speech Separation Based on Deep Learning: An Overview.IEEE/ACM transactions on audio, speech, and language processing · 2018Article
- A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions.The Journal of the Acoustical Society of America · 2018Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Machine learning algorithms to segregate speech from background noise hold considerable promise for alleviating limitations associated with hearing impairment. One of the most important considerations for implementing these algorithms into devices such as hearing aids and cochlear implants involves their ability to generalize to conditions not employed during the training stage. A major challenge involves the generalization to novel noise segments. In the current study, sentences were segregated from multi-talker babble and from cafeteria noise using an algorithm that employs deep neural networks to estimate the ideal ratio mask. Importantly, the algorithm was trained on segments of noise and tested using entirely novel segments of the same nonstationary noise type. Substantial sentence-intelligibility benefit was observed for hearing-impaired listeners in both noise types, despite the use of unseen noise segments during the test stage. Interestingly, normal-hearing listeners displayed benefit in babble but not in cafeteria noise. This result highlights the importance of evaluating these algorithms not only in human subjects, but in members of the actual target population.
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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.