Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition
This work addresses improving speech recognition accuracy with limited labeled data, offering incremental improvements over existing teacher-student methods.
The paper tackles semi-supervised speech recognition by introducing the Kaizen framework, which uses a continuously improving teacher via exponential moving average to generate pseudo-labels, resulting in over 10% relative WER reduction and closing the gap to supervised systems with minimal labeled data.
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.