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.
@article{arxiv.2210.15876,
title = {Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition},
author = {Yist Y. Lin and Tao Han and Haihua Xu and Van Tung Pham and Yerbolat Khassanov and Tze Yuang Chong and Yi He and Lu Lu and Zejun Ma},
journal= {arXiv preprint arXiv:2210.15876},
year = {2023}
}