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.

该研究提出了一种基于 CTC 和 PC-MLM 的自动语音识别方法，该方法综合考虑了速度和准确率，并在实验评估中表现出更高的性能。