Section: New Results

Speech Enhancement with a Variational Auto-Encoder

We addressed the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explored 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 auto-encoder 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 developed a Monte Carlo expectation-maximization algorithm for inferring the latent variables in the variational auto-encoder 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.

Website: https://team.inria.fr/perception/research/ieee-mlsp-2018/.