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- W3153492756 abstract "Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR$rightarrow$TTS direction is equipped with a language model reward to penalize the ASR hypotheses before forwarding it to TTS. 2) In the TTS$rightarrow$ASR direction, a hyper-parameter is introduced to scale the attention context from synthesized speech before sending it to ASR to handle out-of-domain data. Training strategies and the effectiveness of the EAT model are explored under out-of-domain data conditions. The results show that EAT reduces the performance gap between supervised and self-supervised training significantly by absolute 2.6% and 2.7% on Librispeech and BABEL respectively." @default.
- W3153492756 created "2021-04-26" @default.
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- W3153492756 date "2021-04-13" @default.
- W3153492756 modified "2023-09-26" @default.
- W3153492756 title "EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition" @default.
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