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arXiv · 2110.00678

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

Toshiko Shibano·Xinyi Zhang·Mia Taige Li·Haejin Cho·Peter Sullivan·Muhammad Abdul-Mageed
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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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BibTeXRIS

Toshiko Shibano, Xinyi Zhang, Mia Taige Li, Haejin Cho, Peter Sullivan, Muhammad Abdul-Mageed. 2021-10-01. Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning. https://arxiv.org/abs/2110.00678

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