Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders

Mostafa Sadeghi; Xavier Alameda-Pineda

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arXiv · 1911.03930

Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders

Mostafa Sadeghi·Xavier Alameda-Pineda

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Abstract

Recently, an audio-visual speech generative model based on variational autoencoder (VAE) has been proposed, which is combined with a nonnegative matrix factorization (NMF) model for noise variance to perform unsupervised speech enhancement. When visual data is clean, speech enhancement with audio-visual VAE shows a better performance than with audio-only VAE, which is trained on audio-only data. However, audio-visual VAE is not robust against noisy visual data, e.g., when for some video frames, speaker face is not frontal or lips region is occluded. In this paper, we propose a robust unsupervised audio-visual speech enhancement method based on a per-frame VAE mixture model. This mixture model consists of a trained audio-only VAE and a trained audio-visual VAE. The motivation is to skip noisy visual frames by switching to the audio-only VAE model. We present a variational expectation-maximization method to estimate the parameters of the model. Experiments show the promising performance of the proposed method.

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BibTeX RIS

Mostafa Sadeghi, Xavier Alameda-Pineda. 2019-11-10. Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders. https://arxiv.org/abs/1911.03930

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