Learning Speech Representation from Contrastive Token-Acoustic Pretraining-Reference-Cited by-同舟云学术

Learning Speech Representation from Contrastive Token-Acoustic Pretraining

Published:2024-04-14 Issue: Volume: Page:10196-10200
ISSN:
Container-title:ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Qiang Chunyu¹,Li Hao²,Tian Yixin²,Fu Ruibo³,Wang Tao³,Wang Longbiao¹,Dang Jianwu⁴

Affiliation:

1. Tianjin University,School of New Media and Communication,Tianjin,China

2. Kuaishou Technology Co., Ltd,Beijing,China

3. Chinese Academy of Sciences,Institute of Automation,Beijing,China

4. Tianjin University,Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing,Tianjin,China

Publisher

IEEE

Link

Reference21 articles.

2. wav2vec 2.0: A framework for self-supervised learning of speech representations;Baevski;Advances in neural information processing systems,2020

4. vq-wav2vec: Self-supervised learning of discrete speech representations;Baevski,2019

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1. High-Fidelity Speech Synthesis with Minimal Supervision: All Using Diffusion Models;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14