2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-51.6
Paper Title TOWARDS AN ASR APPROACH USING ACOUSTIC AND LANGUAGE MODELS FOR SPEECH ENHANCEMENT
Authors Khandokar Md. Nayem, Donald S. Williamson, Indiana University, United States
SessionSPE-51: Speech Enhancement 7: Single-channel Processing
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Speech Processing: [SPE-ENHA] Speech Enhancement and Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Recent work has shown that deep-learning based speech enhancement performs best when a time-frequency mask is estimated. Unlike speech, these masks have a small range of values that better facilitate regression-based learning. The question remains whether neural-network based speech estimation should be treated as a regression problem. In this work, we propose to modify the speech estimation process, by treating speech enhancement as a classification problem in an ASR-style manner. More specifically, we propose a quantized speech prediction model that classifies speech spectra into a corresponding quantized class. We then train and apply a language-style model that learns the transition probabilities of the quantized classes to ensure more realistic speech spectra. We compare our approach against time-frequency masking approaches, and the results show that our quantized spectra approach leads to improvements.