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- W4200634592 abstract "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 reduce the relative word error rates by 16.3% and 4.6% with the S2S-T and T-T, respectively. Moreover, we thoroughly investigate the effect of the MoE on the T-T architecture in various conditions: streaming mode, non-streaming mode, the use of language ID and the label decoder with the MoE." @default.
- W4200634592 created "2021-12-31" @default.
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- W4200634592 date "2021-12-10" @default.
- W4200634592 modified "2023-10-05" @default.
- W4200634592 title "Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition" @default.
- W4200634592 doi "https://doi.org/10.48550/arxiv.2112.05820" @default.
- W4200634592 hasPublicationYear "2021" @default.
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