  Recently, self-supervised pre-training has gained success in automatic speech
recognition (ASR). However, considering the difference between speech accents
in real scenarios, how to identify accents and use accent features to improve
ASR is still challenging. In this paper, we employ the self-supervised
pre-training method for both accent identification and accented speech
recognition tasks. For the former task, a standard deviation constraint loss
(SDC-loss) based end-to-end (E2E) architecture is proposed to identify accents
under the same language. As for accented speech recognition task, we design an
accent-dependent ASR system, which can utilize additional accent input
features. Furthermore, we propose a frame-level accent feature, which is
extracted based on the proposed accent identification model and can be
dynamically adjusted. We pre-train our models using 960 hours unlabeled
LibriSpeech dataset and fine-tune them on AESRC2020 speech dataset. The
experimental results show that our proposed accent-dependent ASR system is
significantly ahead of the AESRC2020 baseline and achieves $6.5\%$ relative
word error rate (WER) reduction compared with our accent-independent ASR
system.

Audio and Speech Processing;Sound