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This paper describes the AISpeech-SJTU system for the accent identificationtrack of the Interspeech-2020 Accented English Speech Recognition Challenge. Inthis challenge track, only 160-hour accented English data collected from 8countries and the auxiliary Librispeech dataset are provided for training. Tobuild an accurate and robust accent identification system, we explore the wholesystem pipeline in detail. First, we introduce the ASR based phoneposteriorgram (PPG) feature to accent identification and verify its efficacy.Then, a novel TTS based approach is carefully designed to augment the verylimited accent training data for the first time. Finally, we propose the testtime augmentation and embedding fusion schemes to further improve the systemperformance. Our final system is ranked first in the challenge and outperformsall the other participants by a large margin. The submitted system achieves83.63\% average accuracy on the challenge evaluation data, ahead of the othersby more than 10\% in absolute terms.

AISPEECH-SJTU accent identification system for the Accented English Speech Recognition Challenge