ArticleIEEE/ACM transactions on audio, speech, and language processing2021
Speech Intelligibility Prediction using Spectro-Temporal Modulation Analysis.
Article in IEEE/ACM transactions on audio, speech, and language processing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Attenuation and distortion components of age-related hearing loss: Contributions to recognizing temporal-envelope filtered speech in modulated noise.The Journal of the Acoustical Society of America · 2024Article
- Speech emotion analysis using convolutional neural network (CNN) and gamma classifier-based error correcting output codes (ECOC).Scientific reports · 2023Article
- Sentence recognition with modulation-filtered speech segments for younger and older adults: Effects of hearing impairment and cognition.The Journal of the Acoustical Society of America · 2023Article
- Modeling the effect of linguistic predictability on speech intelligibility prediction.JASA express letters · 2023Article
- Spectro-temporal modulation glimpsing for speech intelligibility prediction.Hearing research · 2022Review
- Relating Suprathreshold Auditory Processing Abilities to Speech Understanding in Competition.Brain sciences · 2022Article
- Microscopic and Blind Prediction of Speech Intelligibility: Theory and Practice.IEEE/ACM transactions on audio, speech, and language processing · 2022Article
- Glimpsing keywords across sentences in noise: A microstructural analysis of acoustic, lexical, and listener factors.The Journal of the Acoustical Society of America · 2021Article
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4 authors.
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Abstract
Spectro-temporal modulations are believed to mediate the analysis of speech sounds in the human primary auditory cortex. Inspired by humans' robustness in comprehending speech in challenging acoustic environments, we propose an intrusive speech intelligibility prediction (SIP) algorithm, wSTMI, for normal-hearing listeners based on spectro-temporal modulation analysis (STMA) of the clean and degraded speech signals. In the STMA, each of 55 modulation frequency channels contributes an intermediate intelligibility measure. A sparse linear model with parameters optimized using Lasso regression results in combining the intermediate measures of 8 of the most salient channels for SIP. In comparison with a suite of 10 SIP algorithms, wSTMI performs consistently well across 13 datasets, which together cover degradation conditions including modulated noise, noise reduction processing, reverberation, near-end listening enhancement, and speech interruption. We show that the optimized parameters of wSTMI may be interpreted in terms of modulation transfer functions of the human auditory system. Thus, the proposed approach offers evidence affirming previous studies of the perceptual characteristics underlying speech signal intelligibility.
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