Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

Erica Cooper; Cheng-I Lai; Yusuke Yasuda; Fuming Fang; Xin Wang; Nanxin Chen; Junichi Yamagishi

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arXiv · 1910.10838

Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

Erica Cooper·Cheng-I Lai·Yusuke Yasuda·Fuming Fang·Xin Wang·Nanxin Chen·Junichi Yamagishi

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Abstract

While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers. We investigate multi-speaker modeling for end-to-end text-to-speech synthesis and study the effects of different types of state-of-the-art neural speaker embeddings on speaker similarity for unseen speakers. Learnable dictionary encoding-based speaker embeddings with angular softmax loss can improve equal error rates over x-vectors in a speaker verification task; these embeddings also improve speaker similarity and naturalness for unseen speakers when used for zero-shot adaptation to new speakers in end-to-end speech synthesis.

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BibTeX RIS

Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, Junichi Yamagishi. 2019-10-23. Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings. https://arxiv.org/abs/1910.10838

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