  This paper describes the AISpeech-SJTU system for the accent identification
track of the Interspeech-2020 Accented English Speech Recognition Challenge. In
this challenge track, only 160-hour accented English data collected from 8
countries and the auxiliary Librispeech dataset are provided for training. To
build an accurate and robust accent identification system, we explore the whole
system pipeline in detail. First, we introduce the ASR based phone
posteriorgram (PPG) feature to accent identification and verify its efficacy.
Then, a novel TTS based approach is carefully designed to augment the very
limited accent training data for the first time. Finally, we propose the test
time augmentation and embedding fusion schemes to further improve the system
performance. Our final system is ranked first in the challenge and outperforms
all the other participants by a large margin. The submitted system achieves
83.63\% average accuracy on the challenge evaluation data, ahead of the others
by more than 10\% in absolute terms.
