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arXiv · 1910.10942

A Recurrent Variational Autoencoder for Speech Enhancement

Simon Leglaive·Xavier Alameda-Pineda·Laurent Girin·Radu Horaud
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Abstract

This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE). The deep generative speech model is trained using clean speech signals only, and it is combined with a nonnegative matrix factorization noise model for speech enhancement. We propose a variational expectation-maximization algorithm where the encoder of the RVAE is fine-tuned at test time, to approximate the distribution of the latent variables given the noisy speech observations. Compared with previous approaches based on feed-forward fully-connected architectures, the proposed recurrent deep generative speech model induces a posterior temporal dynamic over the latent variables, which is shown to improve the speech enhancement results.

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BibTeXRIS

Simon Leglaive, Xavier Alameda-Pineda, Laurent Girin, Radu Horaud. 2019-10-24. A Recurrent Variational Autoencoder for Speech Enhancement. https://arxiv.org/abs/1910.10942

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