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Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Herewe propose an enhanced ASR-TTS (EAT) model that incorporates two main features:1) The ASR$\rightarrow$TTS direction is equipped with a language model rewardto penalize the ASR hypotheses before forwarding it to TTS. 2) In theTTS$\rightarrow$ASR direction, a hyper-parameter is introduced to scale theattention context from synthesized speech before sending it to ASR to handleout-of-domain data. Training strategies and the effectiveness of the EAT modelare explored under out-of-domain data conditions. The results show that EATreduces the performance gap between supervised and self-supervised trainingsignificantly by absolute 2.6\% and 2.7\% on Librispeech and BABELrespectively.

EAT: Enhanced ASR-TTS for Self-supervised Speech Recognition