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VAE-based regularization for deep speaker embedding

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arxiv 1904.03617 v1 pith:MSSRQSNA submitted 2019-04-07 cs.SD cs.LGeess.AS

Yang Zhang , Lantian Li , Dong Wang This is my paper · ORCID
classification cs.SDcs.LGeess.AS
keywords speakerdeepembeddinggaussianlatentperformancepldaregularization
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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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Cited by 1 Pith paper

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  1. VAE-based Domain Adaptation for Speaker Verification

    eess.AS 2019-08 conditional novelty 4.0 of 10

    Adapting a VAE normalization model on out-of-domain x-vectors improved speaker verification EER from 18.51% to 12.73% on a small proprietary test set.

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