Deep Q-network-based noise suppression for robust speech recognitionopen access
- Authors
- Park, Tae-Jun; Chang, Joon-Hyuk
- Issue Date
- Apr-2021
- Publisher
- Turkiye Klinikleri
- Keywords
- Deep neural network; Deep Q-network; Noise suppression; Reinforcement learning; Speech enhancement; Speech recognition
- Citation
- Turkish Journal of Electrical Engineering and Computer Sciences, v.25, no.9, pp.2362 - 2373
- Indexed
- SCIE
SCOPUS
- Journal Title
- Turkish Journal of Electrical Engineering and Computer Sciences
- Volume
- 25
- Number
- 9
- Start Page
- 2362
- End Page
- 2373
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142006
- DOI
- 10.3906/ELK-2011-144
- ISSN
- 1300-0632
- Abstract
- This study develops the deep Q-network (DQN)-based noise suppression for robust speech recognition purposes under ambient noise. We thus design a reinforcement algorithm that combines DQN training with a deep neural networks (DNN) to let reinforcement learning (RL) work for complex and high dimensional environments like speech recognition. For this, we elaborate on the DQN training to choose the best action that is the quantized noise suppression gain by the observation of noisy speech signal with the rewards of DQN including both the word error rate (WER) and objective speech quality measure. Experiments demonstrate that the proposed algorithm improves speech recognition in various noisy conditions while reducing the computational burden compared to the DNN-based noise suppression method.
- Files in This Item
-
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/142006)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.