Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition
Abstract
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment. This has led to breakthroughs in many complex tasks, such as playing the game "Go", that were previously difficult to solve. However, deep RL requires significant training time making it difficult to use in various real-life applications such as Human-Computer Interaction (HCI). In this paper, we study pre-training in deep RL to reduce the training time and improve the performance of Speech Recognition, a popular application of HCI. To evaluate the performance improvement in training we use the publicly available "Speech Command" dataset, which contains utterances of 30 command keywords spoken by 2,618 speakers. Results show that pre-training with deep RL offers faster convergence compared to non-pre-trained RL while achieving improved speech recognition accuracy.
Keywords
Cite
@article{arxiv.2005.11172,
title = {Deep Reinforcement Learning with Pre-training for Time-efficient Training of Automatic Speech Recognition},
author = {Thejan Rajapakshe and Siddique Latif and Rajib Rana and Sara Khalifa and Björn W. Schuller},
journal= {arXiv preprint arXiv:2005.11172},
year = {2020}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1910.11256