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

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