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Chung-Cheng Chiu
Researcher at Google
Publications - 93
Citations - 9577
Chung-Cheng Chiu is an academic researcher from Google. The author has contributed to research in topics: Word error rate & Language model. The author has an hindex of 30, co-authored 87 publications receiving 4652 citations. Previous affiliations of Chung-Cheng Chiu include University of Southern California & Institute for Creative Technologies.
Papers
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Proceedings ArticleDOI
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
TL;DR: This work presents SpecAugment, a simple data augmentation method for speech recognition that is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients) and achieves state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work.
Posted Content
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati,James Qin,Chung-Cheng Chiu,Niki Parmar,Yu Zhang,Jiahui Yu,Wei Han,Shibo Wang,Zhengdong Zhang,Yonghui Wu,Ruoming Pang +10 more
TL;DR: This work proposes the convolution-augmented transformer for speech recognition, named Conformer, which significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies.
Proceedings ArticleDOI
State-of-the-Art Speech Recognition with Sequence-to-Sequence Models
Chung-Cheng Chiu,Tara N. Sainath,Yonghui Wu,Rohit Prabhavalkar,Patrick Nguyen,Zhifeng Chen,Anjuli Kannan,Ron Weiss,Kanishka Rao,Ekaterina Gonina,Navdeep Jaitly,Bo Li,Jan Chorowski,Michiel Bacchiani +13 more
TL;DR: In this article, the authors explore a variety of structural and optimization improvements to the Listen, Attend, and Spell (LAS) encoder-decoder architecture, which significantly improves performance.
Proceedings ArticleDOI
Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati,James Qin,Chung-Cheng Chiu,Niki Parmar,Yu Zhang,Jiahui Yu,Wei Han,Shibo Wang,Zhengdong Zhang,Yonghui Wu,Ruoming Pang +10 more
TL;DR: Conformer as mentioned in this paper combines convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way, achieving state-of-the-art accuracies.
Proceedings ArticleDOI
Improved Noisy Student Training for Automatic Speech Recognition
TL;DR: This work adapt and improve noisy student training for automatic speech recognition, employing (adaptive) SpecAugment as the augmentation method and finding effective methods to filter, balance and augment the data generated in between self-training iterations.