End-to-end (E2E) systems have shown comparable performance to hybrid systems
for automatic speech recognition (ASR). Word timings, as a by-product of ASR,
are essential in many applications, especially for subtitling and
computer-aided pronunciation training. In this paper, we improve the
frame-level classifier for word timings in E2E system by introducing label
priors in connectionist temporal classification (CTC) loss, which is adopted
from prior works, and combining low-level Mel-scale filter banks with
high-level ASR encoder output as input feature. On the internal Chinese corpus,
the proposed method achieves 95.68%/94.18% compared to the hybrid system
93.0%/90.22% on the word timing accuracy metrics. It also surpass a previous
E2E approach with an absolute increase of 4.80%/8.02% on the metrics on 7
languages. In addition, we further improve word timing accuracy by delaying CTC
peaks with frame-wise knowledge distillation, though only experimenting on
LibriSpeech.

本文提出了一种在 E2E 系统中改进字时分类器的方法，该方法采用了传统的 CTC 损失连接与低级别 Mel-scale 滤波器和高级 ASR 编码器输出相结合的输入特征。与混合系统和之前的 E2E 方法相比，在内部中文语料库上，提出的方法在字时准确性指标上分别取得了 95.68％/94.18％的成绩，并通过延迟 CTC 峰值来进一步提高了字时准确性。