Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings
CrossMind.ai logo

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

Jan 15, 2021
|
32 views
|
Details
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. Authors: Erica Cooper, Cheng-I Lai, Yusuke Yasuda, Fuming Fang, Xin Wang, Nanxin Chen, Junichi Yamagishi (National Institute of Informatics, Massachusetts Institute of Technology, Johns Hopkins University)

Comments
loading...