A weighted-variance variational autoencoder model for speech enhancement

• URL: http://arxiv.org/abs/2211.00990v2
• Date: Thu, 26 Oct 2023 11:47:25 GMT
• Title: A weighted-variance variational autoencoder model for speech enhancement
• Authors: Ali Golmakani (MULTISPEECH), Mostafa Sadeghi (MULTISPEECH), Xavier Alameda-Pineda (ROBOTLEARN), Romain Serizel (MULTISPEECH)
• Abstract summary: We propose a weighted variance generative model, where the contribution of each spectrogram time-frame in parameter learning is weighted. We develop efficient training and speech enhancement algorithms based on the proposed generative model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued Gaussian distribution is usually assumed for the generative model, where the speech information is encoded in the variance as a function of a latent variable. In contrast to this commonly used approach, we propose a weighted variance generative model, where the contribution of each spectrogram time-frame in parameter learning is weighted. We impose a Gamma prior distribution on the weights, which would effectively lead to a Student's t-distribution instead of Gaussian for speech generative modeling. We develop efficient training and speech enhancement algorithms based on the proposed generative model. Our experimental results on spectrogram auto-encoding and speech enhancement demonstrate the effectiveness and robustness of the proposed approach compared to the standard unweighted variance model.

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