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DGC-vector: A new speaker embedding for zero-shot voice conversion

2022-03-18 03:38:34
Ruitong Xiao, Haitong Zhang, Yue Lin
arXiv_SD
         
Abstract

Recently, more and more zero-shot voice conversion algorithms have been proposed. As a fundamental part of zero-shot voice conversion, speaker embeddings are the key to improving the converted speech's speaker similarity. In this paper, we study the impact of speaker embeddings on zero-shot voice conversion performance. To better represent the characteristics of the target speaker and improve the speaker similarity in zero-shot voice conversion, we propose a novel speaker representation method in this paper. Our method combines the advantages of D-vector, global style token (GST) based speaker representation and auxiliary supervision. Objective and subjective evaluations show that the proposed method achieves a decent performance on zero-shot voice conversion and significantly improves speaker similarity over D-vector and GST-based speaker embedding.

Abstract (translated)

URL

https://arxiv.org/abs/2203.09722

PDF

https://arxiv.org/pdf/2203.09722.pdf

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