| Paper ID | SPE-28.2 |
| Paper Title |
AISPEECH-SJTU ASR system for the Accented English Speech Recognition Challenge |
| Authors |
Tian Tan, Aispeech, China; Yizhou Lu, Rao Ma, Shanghai Jiao Tong University, China; Sen Zhu, Jiaqi Guo, Aispeech, China; Yanmin Qian, Shanghai Jiao Tong University, China |
| Session | SPE-28: Speech Recognition 10: Robustness to Human Speech Variability |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-ROBU] Robust Speech Recognition |
| 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 ASR system for the Interspeech-2020 Accented English Speech Recognition Challenge~(AESRC). This task is challenging due to the diversity of pronunciation accuracy, intonation speed and pronunciation of some syllables. All participants were restricted to develop their systems based on the speech and text corpora provided by the organizer. To work around the data-scarcity problem, data augmentation was first explored including noise simulation, SpecAugment, speed perturbation and TTS simulation. Moreover, SOTA CNN-transformer-based joint CTC-attention system was built and accent adaptation was proposed to train an accent robust system. Finally, the first-pass recognition hypotheses generated from CTC head were rescored by forward, backward LSTM-LM and the attention head. Our system with the best configuration achieves second place in the challenge, resulting in a word error rate (WER) of 4.00\% on dev set and 4.47\% WER on test set, while WER on test set of the top-performing, second runner-up and official baseline systems are 4.06\%, 4.52\%, 8.29\%, respectively. |
