Existing training criteria in automatic speech recognition(ASR) permit the model to freely explore more than one time alignments between the feature and label sequences. In this paper, we use entropy to measure a model's uncertainty, i.e. how it chooses to distribute the probability mass over the set of allowed alignments. Furthermore, we evaluate the effect of entropy regularization in encouraging the model to distribute the probability mass only on a smaller subset of allowed alignments. Experiments show that entropy regularization enables a much simpler decoding method without sacrificing word error rate, and provides better time alignment quality.

翻译：自动语音识别( ASR) 中的现有培训标准允许模型自由探索特性和标签序列之间的一次调整。 在本文中, 我们使用 entropy 来测量模型的不确定性, 即它如何选择在一组允许的对齐中分配概率质量 。 此外, 我们评估了 entropy 正规化在鼓励模型仅将概率质量分布在较小的允许对齐子上的效果 。 实验显示, entropy 正规化可以使解码方法简单得多, 不牺牲单词错误率, 并提供更好的时间对齐质量 。