  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.
