  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). In the proposed
method, less confident word tokens in a greedy decoded output from CTC are
masked. PC-MLM then predicts these masked word tokens given unmasked words and
phones supplementally predicted from CTC. We further extend it to Deletable
PC-MLM in order to address insertion errors. Since both CTC and PC-MLM are
non-autoregressive models, the method enables fast LM integration. Experimental
evaluations on the Corpus of Spontaneous Japanese (CSJ) and TED-LIUM2 in domain
adaptation setting shows that our proposed method outperformed rescoring and
shallow fusion in terms of inference speed, and also in terms of recognition
accuracy on CSJ.
