In this work, we describe a novel method of training an embedding-matching
word-level connectionist temporal classification (CTC) automatic speech
recognizer (ASR) such that it directly produces word start times and durations,
required by many real-world applications, in addition to the transcription. The
word timestamps enable the ASR to output word segmentations and word confusion
networks without relying on a secondary model or forced alignment process when
testing. Our proposed system has similar word segmentation accuracy as a hybrid
DNN-HMM (Deep Neural Network-Hidden Markov Model) system, with less than 3ms
difference in mean absolute error in word start times on TIMIT data. At the
same time, we observed less than 5% relative increase in the word error rate
compared to the non-timestamped system when using the same audio training data
and nearly identical model size. We also contribute more rigorous analysis of
multiple-hypothesis embedding-matching ASR in general.

本文提出了一种新颖的嵌入匹配语音识别器，它直接产生了单词的起始时间和持续时间，并可以在测试时输出单词分割和单词混淆网络，同时相对于非时间戳系统，Word timestamps 可以使得系统获得类似于混合 DNN-HMM 系统的单词分割准确度和不到 3ms 的较小差异，我们提供了多重假设嵌入匹配 ASR 的更严格的分析。