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Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition. (arXiv:2112.05820v3 [cs.CL] UPDATED)
Jan. 5, 2022, 2:10 a.m. | Kenichi Kumatani, Robert Gmyr, Felipe Cruz Salinas, Linquan Liu, Wei Zuo, Devang Patel, Eric Sun, Yu Shi
cs.LG updates on arXiv.org arxiv.org
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity
with a little computational complexity. In this work, we investigate how
multi-lingual Automatic Speech Recognition (ASR) networks can be scaled up with
a simple routing algorithm in order to achieve better accuracy. More
specifically, we apply the sparsely-gated MoE technique to two types of
networks: Sequence-to-Sequence Transformer (S2S-T) and Transformer Transducer
(T-T). We demonstrate through a set of ASR experiments on multiple language
data that the MoE networks can …
More from arxiv.org / cs.LG updates on arXiv.org
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