# Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders

## TL;DR

This paper introduces an audio-visual conditional variational auto-encoder for speech enhancement that leverages visual lip information to improve performance, especially in noisy conditions, outperforming existing methods.

## Contribution

It proposes a novel audio-visual CVAE model conditioned on lip visuals for unsupervised speech enhancement, combining generative modeling with noise modeling techniques.

## Key findings

- Audio-visual CVAE outperforms audio-only VAE in noisy conditions.
- The method surpasses state-of-the-art supervised deep learning approaches.
- Visual information significantly improves speech enhancement quality.

## Abstract

Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. One advantage of this generative approach is that it does not require pairs of clean and noisy speech signals at training. In this paper, we propose audio-visual variants of VAEs for single-channel and speaker-independent speech enhancement. We develop a conditional VAE (CVAE) where the audio speech generative process is conditioned on visual information of the lip region. At test time, the audio-visual speech generative model is combined with a noise model based on nonnegative matrix factorization, and speech enhancement relies on a Monte Carlo expectation-maximization algorithm. Experiments are conducted with the recently published NTCD-TIMIT dataset as well as the GRID corpus. The results confirm that the proposed audio-visual CVAE effectively fuses audio and visual information, and it improves the speech enhancement performance compared with the audio-only VAE model, especially when the speech signal is highly corrupted by noise. We also show that the proposed unsupervised audio-visual speech enhancement approach outperforms a state-of-the-art supervised deep learning method.