Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM

Understand

Connectionist temporal classification (CTC) -based models are attractive in automatic speech recognition (ASR) because of their non-autoregressive nature.

• To take advantage of text-only data, language model (LM) integration approaches such as rescoring and shallow fusion have been widely used for CTC.
• However, they lose CTC's non-autoregressive nature because of the need for beam search, which slows down the inference speed.
• In this study, we propose an error correction method with phone-conditioned masked LM (PC-MLM).