Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection

TL;DR

This paper introduces a reinforcement learning framework for speech recognition that reduces reliance on costly transcribed data, enabling systems to learn effectively from minimal user feedback.

Contribution

It proposes a novel reinforcement learning approach based on policy gradients and hypothesis selection, offering a new perspective on training and adapting speech recognition systems.

Findings

Improved recognition performance over unsupervised adaptation

Framework reduces need for extensive transcribed data

Demonstrates effectiveness of reinforcement learning in speech recognition

Abstract

Speech recognition systems have achieved high recognition performance for several tasks. However, the performance of such systems is dependent on the tremendously costly development work of preparing vast amounts of task-matched transcribed speech data for supervised training. The key problem here is the cost of transcribing speech data. The cost is repeatedly required to support new languages and new tasks. Assuming broad network services for transcribing speech data for many users, a system would become more self-sufficient and more useful if it possessed the ability to learn from very light feedback from the users without annoying them. In this paper, we propose a general reinforcement learning framework for speech recognition systems based on the policy gradient method. As a particular instance of the framework, we also propose a hypothesis selection-based reinforcement learning method. The proposed framework provides a new view for several existing training and adaptation methods. The experimental results show that the proposed method improves the recognition performance compared to unsupervised adaptation.