| Paper ID | SPE-22.6 |
| Paper Title |
AISPEECH-SJTU ACCENT IDENTIFICATION SYSTEM FOR THE ACCENTED ENGLISH SPEECH RECOGNITION CHALLENGE |
| Authors |
Houjun Huang, Xu Xiang, AISpeech Ltd, Suzhou, China; Yexin Yang, Rao Ma, Yanmin Qian, Shanghai Jiao Tong University, China |
| Session | SPE-22: Speech Recognition 8: Multilingual Speech Recognition |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-MULT] Multilingual Recognition and Identification |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
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. |
