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Open Access

Optimizing the Perceptual Quality of Time-Domain Speech Enhancement with Reinforcement Learning

School of Computer Science, Northwestern Polytechnical University, Xi’an 710000, China
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 710129, Singapore
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Abstract

In neural speech enhancement, a mismatch exists between the training objective, i.e., Mean-Square Error (MSE), and perceptual quality evaluation metrics, i.e., perceptual evaluation of speech quality and short-time objective intelligibility. We propose a novel reinforcement learning algorithm and network architecture, which incorporate a non-differentiable perceptual quality evaluation metric into the objective function using a dynamic filter module. Unlike the traditional dynamic filter implementation that directly generates a convolution kernel, we use a filter generation agent to predict the probability density function of a multivariate Gaussian distribution, from which we sample the convolution kernel. Experimental results show that the proposed reinforcement learning method clearly improves the perceptual quality over other supervised learning methods with the MSE objective function.

Keywords

speech enhancementneural networksdynamic filterreinforcement learning

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Tsinghua Science and Technology
Volume 27 Issue 6,
December 2022
Pages 939-947
DOI: 10.26599/TST.2021.9010048
Cite this article:
Hao X, Xu C, Xie L, et al. Optimizing the Perceptual Quality of Time-Domain Speech Enhancement with Reinforcement Learning. Tsinghua Science and Technology, 2022, 27(6): 939-947. https://doi.org/10.26599/TST.2021.9010048

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Received: 02 March 2021
Revised: 08 June 2021
Accepted: 12 July 2021
Published: 21 June 2022
© The author(s) 2022.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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