Universal Cross-Lingual Data Generation for Low Resource ASR
Author:
Affiliation:
1. Auditory Cognition and Computational Acoustics Lab, the Department of Computer Science and Engineering and the MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China
Funder
China NSFC projects
Shanghai Municipal Science and Technology Commission Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics
Link
http://xplorestaging.ieee.org/ielx7/6570655/10304349/10372076.pdf?arnumber=10372076
Reference56 articles.
1. Improved Training of End-to-end Attention Models for Speech Recognition
2. SpeechStew: Simply mix all available speech recognition data to train one large neural network;Chan,2021
3. Robust speech recognition via large-scale weak supervision;Radford,2023
4. Recent Advances in End-to-End Automatic Speech Recognition
5. Transfer Learning for Speech Recognition on a Budget
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