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1. BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition Yu Zhang , Daniel S. Park , Wei Han , James Qin, Anmol Gulati, Joel Shor, Aren Jansen, Yuanzhong Xu, Yanping Huang, Shibo Wang, Zongwei Zhou, Bo Li, Min Ma, William Chan, Jiahui Yu, Yongqiang Wang, Liangliang Cao, Khe Chai Sim, Bhuvana Ramabhadran, [ ] 1 Oct 2021. Tara N. Sainath, Fran oise Beaufays, Zhifeng Chen, Quoc V. Le, Chung-Cheng Chiu, Ruoming Pang and Yonghui Wu Abstract We summarize the results of a host of efforts using capacity to different domains in some instances [30]. Second, giant automatic speech recognition (ASR) models pre-trained the Libri-Light dataset is not much bigger than industrial- using large, diverse unlabeled datasets containing approximately scaled labeled datasets.
studying the effect of the labeled dataset size, model size, pre-training, upstream and downstream self-training. Cross-lingual benefits of pre-training are also explored. Non-ASR Tasks: We use features derived from the interme-diate layers of the P-models for non-ASR tasks. We achieve SoTA performance for multiple tasks within the non-semantic
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