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Journal Article
Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
- Authors
- Hoon Chung, Sung Joo Lee, Hyeong Bae Jeon, Jeon Gue Park
- Issue Date
- 2020-05
- Citation
- Applied Sciences, v.10, no.10, pp.1-13
- ISSN
- 2076-3417
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Based Approach, Behavior strategy, Confidence measure, External knowledge, Post-Processing, Pre-trained model, Pseudo labels, Reinforcement learning(RL), Reinforcement learning algorithm, Student Learning, Training cycle
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
