AccentSpeech: Learning Accent from Crowd-sourced Data for Target Speaker TTS with Accents-Reference-Cited by-同舟云学术

AccentSpeech: Learning Accent from Crowd-sourced Data for Target Speaker TTS with Accents

Published:2022-12-11 Issue: Volume: Page:
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Container-title:2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP)
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Author:

Zhang Yongmao¹,Wang Zhichao¹,Yang Peiji²,Sun Hongshen²,Wang Zhisheng²,Xie Lei¹

Affiliation:

1. Northwestern Polytechnical University,Audio, Speech and Language Processing Group (ASLP@NPU) School of Computer Science,Xi’an,China

2. Tencent,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10037756/10037573/10037914.pdf?arnumber=10037914

Reference26 articles.

1. Referee: Towards Reference-Free Cross-Speaker Style Transfer with Low-Quality Data for Expressive Speech Synthesis

2. An Overview of Noise-Robust Automatic Speech Recognition

3. Kespeech: An open source speech dataset of mandarin and its eight subdialects;tang;Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 NeurIPS Datasets and Benchmarks 2021,2021

4. Cloning one’s voice using very limited data in the wild;dai;IEEE International Conference on Acoustics Speech and Signal Processing ICASSP 2022,2022

5. Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

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