Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition

Vimal Manohar; Tatiana Likhomanenko; Qiantong Xu; Wei-Ning Hsu; Ronan Collobert; Yatharth Saraf; Geoffrey Zweig; Abdelrahman Mohamed

arXiv · 2106.07759

Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition

Vimal Manohar·Tatiana Likhomanenko·Qiantong Xu·Wei-Ning Hsu·Ronan Collobert·Yatharth Saraf·Geoffrey Zweig·Abdelrahman Mohamed

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Abstract

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.

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Vimal Manohar, Tatiana Likhomanenko, Qiantong Xu, Wei-Ning Hsu, Ronan Collobert, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed. 2021-06-14. Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognition. https://arxiv.org/abs/2106.07759

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