Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier

TL;DR

This paper introduces a guided variational autoencoder for speech enhancement that incorporates a supervised classifier trained on noisy speech, leading to improved extraction of speech signals in noisy environments.

Contribution

The paper proposes a novel approach that guides a variational autoencoder with a supervised classifier trained on noisy speech, enhancing its ability to perform speech enhancement.

Findings

Outperforms standard variational autoencoders in noisy environments

Effective use of high-level categorical labels improves speech extraction

Guided VAE surpasses conventional neural network approaches

Abstract

Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variational autoencoders are trained on clean speech only, which results in a limited ability of extracting the speech signal from noisy speech compared to supervised approaches. In this paper, we propose to guide the variational autoencoder with a supervised classifier separately trained on noisy speech. The estimated label is a high-level categorical variable describing the speech signal (e.g. speech activity) allowing for a more informed latent distribution compared to the standard variational autoencoder. We evaluate our method with different types of labels on real recordings of different noisy environments. Provided that the label better informs the latent distribution and that the classifier achieves good performance, the proposed approach outperforms the standard variational autoencoder and a conventional neural network-based supervised approach.