ArticleThe Journal of the Acoustical Society of America2019
Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants.
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 34 papers.
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The trial behind it
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Who cites it
34 citing papers in PubMed.
- Belief embodiment through eye movements facilitates memory-guided navigation.Nature communications · 2025Article
- Speech and music source separation for cochlear implant users: front-end and end-to-end approach.Frontiers in neuroscience · 2025Article
- An Artificial Intelligence-Based Non-Invasive Approach for Cardiovascular Disease Risk Stratification in Obstructive Sleep Apnea Patients: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction.Sensors (Basel, Switzerland) · 2024Article
- Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users.Scientific reports · 2024Article
- UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.Reviews in cardiovascular medicine · 2024Review
- Improved tactile speech robustness to background noise with a dual-path recurrent neural network noise-reduction method.Scientific reports · 2024Article
- Objective intelligibility measurement of reverberant vocoded speech for normal-hearing listeners: Towards facilitating the development of speech enhancement algorithms for cochlear implants.The Journal of the Acoustical Society of America · 2024Article
- Improved tactile speech perception using audio-to-tactile sensory substitution with formant frequency focusing.Scientific reports · 2024Article
- Experimental Investigation of Acoustic Features to Optimize Intelligibility in Cochlear Implants.Sensors (Basel, Switzerland) · 2023Article
- Improving speech perception for hearing-impaired listeners using audio-to-tactile sensory substitution with multiple frequency channels.Scientific reports · 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
- Helping People Hear Better with "Smart" Hearing Devices.Frontiers for young minds · 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
- Improved Environment-Aware-Based Noise Reduction System for Cochlear Implant Users Based on a Knowledge Transfer Approach: Development and Usability Study.Journal of medical Internet research · 2021Article
- Cochlear Implant Research and Development in the Twenty-first Century: A Critical Update.Journal of the Association for Research in Otolaryngology : JARO · 2021Review
- 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
- A CAUSAL DEEP LEARNING FRAMEWORK FOR CLASSIFYING PHONEMES IN COCHLEAR IMPLANTS.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise. Noise-reduction algorithms have produced benefits but relied on a priori information about the target speaker and/or background noise. A recurrent neural network (RNN) algorithm was developed for enhancing speech in non-stationary noise and its benefits were evaluated for speech perception, using both objective measures and experiments with CI simulations and CI users. The RNN was trained using speech from many talkers mixed with multi-talker or traffic noise recordings. Its performance was evaluated using speech from an unseen talker mixed with different noise recordings of the same class, either babble or traffic noise. Objective measures indicated benefits of using a recurrent over a feed-forward architecture, and predicted better speech intelligibility with than without the processing. The experimental results showed significantly improved intelligibility of speech in babble noise but not in traffic noise. CI subjects rated the processed stimuli as significantly better in terms of speech distortions, noise intrusiveness, and overall quality than unprocessed stimuli for both babble and traffic noise. These results extend previous findings for CI users to mostly unseen acoustic conditions with non-stationary noise.
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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.