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arXiv · 2102.12664

MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition

Linghui Meng·Jin Xu·Xu Tan·Jindong Wang·Tao Qin·Bo Xu
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Abstract

In this paper, we propose MixSpeech, a simple yet effective data augmentation method based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model by taking a weighted combination of two different speech features (e.g., mel-spectrograms or MFCC) as the input, and recognizing both text sequences, where the two recognition losses use the same combination weight. We apply MixSpeech on two popular end-to-end speech recognition models including LAS (Listen, Attend and Spell) and Transformer, and conduct experiments on several low-resource datasets including TIMIT, WSJ, and HKUST. Experimental results show that MixSpeech achieves better accuracy than the baseline models without data augmentation, and outperforms a strong data augmentation method SpecAugment on these recognition tasks. Specifically, MixSpeech outperforms SpecAugment with a relative PER improvement of 10.6$\%$ on TIMIT dataset, and achieves a strong WER of 4.7$\%$ on WSJ dataset.

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Linghui Meng, Jin Xu, Xu Tan, Jindong Wang, Tao Qin, Bo Xu. 2021-02-25. MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition. https://arxiv.org/abs/2102.12664

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