Systems for Low-Resource Speech Recognition Tasks in Open Automatic Speech Recognition and Formosa Speech Recognition Challenges
|Hung-Pang Lin (National Sun Yat-sen University, Taiwan), Yu-Jia Zhang (National Sun Yat-sen University, Taiwan), Chia-Ping Chen (National Sun Yat-sen University, Taiwan)|
We, in the team name of NSYSU-MITLab, have participated in low-resource speech recognition of the Open Automatic Speech Recognition Challenge 2020 (OpenASR20) and Formosa Speech Recognition Challenge 2020 (FSR-2020). For the tasks in the challenges, we build and compare end-to-end (E2E) systems and Deep Neural Network Hidden Markov Model (DNN-HMM) systems. In E2E systems, we implement an encoder with Conformer architecture and a decoder with Transformer architecture. In addition, a speaker classifier with a gradient reversal layer is included in the training phase to improve the robustness to speaker variation. In DNN-HMM systems, we implement the Time-Restricted Self-Attention and Factorized Time Delay Neural Networks for the DNN front-end acoustic representation learning. In OpenASR20, the best word error rates we achieved are 61.45% for Cantonese and 74.61% for Vietnamese. In FSR-2020, the best character error rate we achieved is 43.4% for Taiwanese Southern Min Recommended Characters and the best syllable error rate is 25.4% for Taiwan Minnanyu Luomazi Pinyin.