Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition

Yist Y. Lin; Tao Han; Haihua Xu; Van Tung Pham; Yerbolat Khassanov; Tze Yuang Chong; Yi He; Lu Lu; Zejun Ma

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arXiv · 2210.15876

Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition

Yist Y. Lin·Tao Han·Haihua Xu·Van Tung Pham·Yerbolat Khassanov·Tze Yuang Chong·Yi He·Lu Lu·Zejun Ma

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Abstract

One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched. In this paper, we propose an on-the-fly random utterance concatenation (RUC) based data augmentation method to alleviate train-test utterance length mismatch issue for short-video ASR task. Specifically, we are motivated by observations that our human-transcribed training utterances tend to be much shorter for short-video spontaneous speech (~3 seconds on average), while our test utterance generated from voice activity detection front-end is much longer (~10 seconds on average). Such a mismatch can lead to suboptimal performance. Empirically, it's observed the proposed RUC method significantly improves long utterance recognition without performance drop on short one. Overall, it achieves 5.72% word error rate reduction on average for 15 languages and improved robustness to various utterance length.

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Yist Y. Lin, Tao Han, Haihua Xu, Van Tung Pham, Yerbolat Khassanov, Tze Yuang Chong, Yi He, Lu Lu, Zejun Ma. 2023-05-25. Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition. https://arxiv.org/abs/2210.15876

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