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。

基于 CTC 的音频语音识别中基于音素掩码的非自回归误差修正

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

Connectionist temporal classification (CTC) -based models are attractive
because of their fast inference in automatic speech recognition (ASR). Language
model (LM) integration approaches such as shallow fusion and rescoring can
improve the recognition accuracy of CTC-based ASR by taking advantage of the
knowledge in text corpora. However, they significantly slow down the inference
of CTC. In this study, we propose to distill the knowledge of BERT for
CTC-based ASR, extending our previous study for attention-based ASR. CTC-based
ASR learns the knowledge of BERT during training and does not use BERT during
testing, which maintains the fast inference of CTC. Different from
attention-based models, CTC-based models make frame-level predictions, so they
need to be aligned with token-level predictions of BERT for distillation. We
propose to obtain alignments by calculating the most plausible CTC paths.
Experimental evaluations on the Corpus of Spontaneous Japanese (CSJ) and
TED-LIUM2 show that our method improves the performance of CTC-based ASR
without the cost of inference speed.

本研究提出使用 BERT 来优化基于 CTC 的自动语音识别，通过计算最合理的 CTC 路径获得对齐，并且不影响 CTC 的快速推理速度。实验结果显示，该方法提高了识别准确率而不影响推理速度。