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

• URL: http://arxiv.org/abs/2110.00678v1
• Date: Fri, 1 Oct 2021 23:11:00 GMT
• Title: Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning
• Authors: Toshiko Shibano (1), Xinyi Zhang (1), Mia Taige Li (1), Haejin Cho (1), Peter Sullivan (1), Muhammad Abdul-Mageed (1) ((1) University of British Columbia)
• Abstract summary: We evaluate fine-tuning of pretrained wav2vec 2.0 models on L2-ARCTIC, a non-native English speech corpus. Our experiments demonstrate the promise of developing ASR models for non-native English speakers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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