One of the limitations in end-to-end automatic speech recognition framework
is its performance would be compromised if train-test utterance lengths are
mismatched. In this paper, we propose a random utterance concatenation (RUC)
method to alleviate train-test utterance length mismatch issue for short-video
speech recognition task. Specifically, we are motivated by observations 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 sub-optimal performance. Experimentally,
by using the proposed RUC method, the best word error rate reduction (WERR) can
be achieved with around three fold training data size increase as well as two
utterance concatenation for each. In practice, the proposed method consistently
outperforms the strong baseline models, where 3.64% average WERR is achieved on
14 languages.

本文提出了一种称为随机语音串接（RUC）的方法，以缓解短视频语音识别任务中的训练 - 测试语音长度不匹配问题，实验证明使用该方法可明显提高模型性能，使得在 14 种语言上的平均词错误率降低了 3.64%。