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Open AccessArticle

Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient

Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(10), 3542;
Received: 13 April 2020 / Revised: 18 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Intelligent Speech and Acoustic Signal Processing)
In this paper, we propose a policy gradient-based semi-supervised speech recognition acoustic model training. In practice, self-training and teacher/student learning are one of the widely used semi-supervised training methods due to their scalability and effectiveness. These methods are based on generating pseudo labels for unlabeled samples using a pre-trained model and selecting reliable samples using confidence measure. However, there are some considerations in this approach. The generated pseudo labels can be biased depending on which pre-trained model is used, and the training process can be complicated because the confidence measure is usually carried out in post-processing using external knowledge. Therefore, to address these issues, we propose a policy gradient method-based approach. Policy gradient is a reinforcement learning algorithm to find an optimal behavior strategy for an agent to obtain optimal rewards. The policy gradient-based approach provides a framework for exploring unlabeled data as well as exploiting labeled data, and it also provides a way to incorporate external knowledge in the same training cycle. The proposed approach was evaluated on an in-house non-native Korean recognition domain. The experimental results show that the method is effective in semi-supervised acoustic model training. View Full-Text
Keywords: speech recognition; semi-supervised training; reinforcement learning; policy gradient speech recognition; semi-supervised training; reinforcement learning; policy gradient
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Chung, H.; Lee, S.J.; Jeon, H.B.; Park, J.G. Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient. Appl. Sci. 2020, 10, 3542.

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