ArticleThe Journal of the Acoustical Society of America2018
A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions.
Article in The Journal of the Acoustical Society of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- 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
- Restoring speech intelligibility for hearing aid users with deep learning.Scientific reports · 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
- 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 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
- 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
- Article
- Sixty Years of Frequency-Domain Monaural Speech Enhancement: From Traditional to Deep Learning Methods.Trends in hearingReview
- Objective Evaluation of a Deep Learning-Based Noise Reduction Algorithm for Hearing Aids Under Diverse Fitting and Listening Conditions.Trends in hearingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Recently, deep learning based speech segregation has been shown to improve human speech intelligibility in noisy environments. However, one important factor not yet considered is room reverberation, which characterizes typical daily environments. The combination of reverberation and background noise can severely degrade speech intelligibility for hearing-impaired (HI) listeners. In the current study, a deep learning based time-frequency masking algorithm was proposed to address both room reverberation and background noise. Specifically, a deep neural network was trained to estimate the ideal ratio mask, where anechoic-clean speech was considered as the desired signal. Intelligibility testing was conducted under reverberant-noisy conditions with reverberation time
Indexed as
Identifiers
What OpenQuestion holds
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.