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arXiv · 1902.01605

A variance modeling framework based on variational autoencoders for speech enhancement

Simon Leglaive·Laurent Girin·Radu Horaud
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Abstract

In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a popular speech variance model based on supervised non-negative matrix factorization (NMF). More precisely, we use a variational autoencoder as a speaker-independent supervised generative speech model, highlighting the conceptual similarities that this approach shares with its NMF-based counterpart. In order to be free of generalization issues regarding the noisy recording environments, we follow the approach of having a supervised model only for the target speech signal, the noise model being based on unsupervised NMF. We develop a Monte Carlo expectation-maximization algorithm for inferring the latent variables in the variational autoencoder and estimating the unsupervised model parameters. Experiments show that the proposed method outperforms a semi-supervised NMF baseline and a state-of-the-art fully supervised deep learning approach.

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BibTeXRIS

Simon Leglaive, Laurent Girin, Radu Horaud. 2019-02-05. A variance modeling framework based on variational autoencoders for speech enhancement. https://doi.org/10.1109/mlsp.2018.8516711

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