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This paper presents BERT-CTC, a novel formulation of end-to-end speechrecognition that adapts BERT for connectionist temporal classification (CTC).Our formulation relaxes the conditional independence assumptions used inconventional CTC and incorporates linguistic knowledge through the explicitoutput dependency obtained by BERT contextual embedding. BERT-CTC attends tothe full contexts of the input and hypothesized output sequences via theself-attention mechanism. This mechanism encourages a model to learninner/inter-dependencies between the audio and token representations whilemaintaining CTC's training efficiency. During inference, BERT-CTC combines amask-predict algorithm with CTC decoding, which iteratively refines an outputsequence. The experimental results reveal that BERT-CTC improves overconventional approaches across variations in speaking styles and languages.Finally, we show that the semantic representations in BERT-CTC are beneficialtowards downstream spoken language understanding tasks.

BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model