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arXiv · 1904.03617

VAE-based regularization for deep speaker embedding

Yang Zhang·Lantian Li·Dong Wang
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Abstract

Deep speaker embedding has achieved state-of-the-art performance in speaker recognition. A potential problem of these embedded vectors (called `x-vectors') are not Gaussian, causing performance degradation with the famous PLDA back-end scoring. In this paper, we propose a regularization approach based on Variational Auto-Encoder (VAE). This model transforms x-vectors to a latent space where mapped latent codes are more Gaussian, hence more suitable for PLDA scoring.

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BibTeXRIS

Yang Zhang, Lantian Li, Dong Wang. 2019-04-07. VAE-based regularization for deep speaker embedding. https://arxiv.org/abs/1904.03617

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