Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning

• Main Authors: Shibano, Toshiko, Zhang, Xinyi, Li, Mia Taige, Cho, Haejin, Sullivan, Peter, Abdul-Mageed, Muhammad
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2021
• Subjects: Audio and Speech Processing eess.AS; Computation and Language cs.CL; Machine Learning cs.LG; FOS Electrical engineering, electronic engineering, information engineering; FOS Computer and information sciences; Arctic
• Online Access: https://dx.doi.org/10.48550/arxiv.2110.00678; https://arxiv.org/abs/2110.00678

To address the performance gap of English ASR models on L2 English speakers, we evaluate fine-tuning of pretrained wav2vec 2.0 models (Baevski et al., 2020; Xu et al., 2021) on L2-ARCTIC, a non-native English speech corpus (Zhao et al., 2018) under different training settings. We compare \textbf{(a)} models trained with a combination of diverse accents to ones trained with only specific accents and \textbf{(b)} results from different single-accent models. Our experiments demonstrate the promise of developing ASR models for non-native English speakers, even with small amounts of L2 training data and even without a language model. Our models also excel in the zero-shot setting where we train on multiple L2 datasets and test on a blind L2 test set. : All authors contributed equally. Paper accepted to International Conference on Natural Language and Speech Processing 2021 (ICNLSP 2021)