A Novel Policy for Pre-trained Deep Reinforcement Learning for Speech Emotion Recognition

Rajapakshe, Thejan ORCID: https://orcid.org/0000-0003-3156-3327 and Rana, Rajib ORCID: https://orcid.org/0000-0002-0506-2409 and Khalifa, Sara and Liu, Jiajun and Schuller, Bjorn W (2022) A Novel Policy for Pre-trained Deep Reinforcement Learning for Speech Emotion Recognition. In: 2022 Australasian Computer Science Week (ACSW 2022), 14 Feb - 17 Feb 2022, Brisbane, Australia.


Abstract

Deep Reinforcement Learning (deep RL) has gained tremendous success in gaming but it has rarely been explored for Speech Emotion Recognition (SER). In the RL literature, policy used by the RL agent plays a major role in action selection, however, there is no RL policy tailored for SER. Also, an extended learning period is a general challenge for deep RL, which can impact the speed of learning for SER. In this paper, we introduce a novel policy, the 'Zeta policy' tailored for SER and introduce pre-training in deep RL to achieve a faster learning rate. Pre-training with a cross dataset was also studied to discover the feasibility of pre-training the RL agent with a similar dataset in a scenario where real environmental data is not available. We use 'IEMOCAP' and 'SAVEE' datasets for the evaluation with the problem of recognising four emotions, namely happy, sad, angry, and neutral. The experimental results show that the proposed policy performs better than existing policies. Results also support that pre-training can reduce training time and is robust to a cross-corpus scenario.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 20 Apr 2022 23:38
Last Modified: 21 Apr 2022 01:24
Uncontrolled Keywords: Speech Emotion Recognition, Reinforcement Learning, Deep Learning, Deep Reinforcement Learning, Machine Learning
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460212 Speech recognition
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461105 Reinforcement learning
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2203 Information services > 220301 Digital humanities
Identification Number or DOI: https://doi.org/10.1145/3511616.3513104
URI: http://eprints.usq.edu.au/id/eprint/47794

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