In this paper, we introduce the Kaizen framework that uses a continuously
improving teacher to generate pseudo-labels for semi-supervised speech
recognition (ASR). The proposed approach uses a teacher model which is updated
as the exponential moving average (EMA) of the student model parameters. We
demonstrate that it is critical for EMA to be accumulated with full-precision
floating point. The Kaizen framework can be seen as a continuous version of the
iterative pseudo-labeling approach for semi-supervised training. It is
applicable for different training criteria, and in this paper we demonstrate
its effectiveness for frame-level hybrid hidden Markov model-deep neural
network (HMM-DNN) systems as well as sequence-level Connectionist Temporal
Classification (CTC) based models.
For large scale real-world unsupervised public videos in UK English and
Italian languages the proposed approach i) shows more than 10% relative word
error rate (WER) reduction over standard teacher-student training; ii) using
just 10 hours of supervised data and a large amount of unsupervised data closes
the gap to the upper-bound supervised ASR system that uses 650h or 2700h
respectively.

介绍了使用 Kaizen 框架的半监督语音识别方法，该方法利用一个不断更新的教师模型为 ASR 生成伪标签，可用于不同培训准则，对于大规模的真实世界无监督公共英语和意大利语视频表现出 10% 以上的相对词误率（WER）降低。