ASR - VLSP 2021: Semi-supervised Ensemble Model for Vietnamese Automatic Speech Recognition

Abstract

Automatic speech recognition (ASR) is gaining huge advances with the arrival of End-to-End architectures. Semi-supervised learning methods, which can utilize unlabeled data, have largely contributed to the success of ASR systems, giving them the ability to surpass human performance. However, most of the researches focus on developing these techniques for English speech recognition, which raises concern about their performance in other languages, especially in low-resource scenarios. In this paper, we aim at proposing a Vietnamese ASR system for participating in the VLSP 2021 Automatic Speech Recognition Shared Task. The system is based on the Wav2vec 2.0 framework, along with the application of self-training and several data augmentation techniques. Experimental results show that on the ASR-T1 test set of the shared task, our proposed model achieved a remarkable result, ranked as the second place with a Syllable Error Rate (SyER) of 11.08%.