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Towards an ASR Approach Using Acoustic and Language Models for Speech Enhancement

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Last updated:
24 June 2021 - 4:06pm
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Presentation Slides
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Presenters Name:
Khandokar Md. Nayem
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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.

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Dataset Files

Presentation slides of ASR style quantized spectral model based SE, presented at ICASSP 2021.