Domain Adapting Speech Emotion Recognition modals to real-world scenario with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2207.12248v1
• Date: Thu, 7 Jul 2022 02:53:39 GMT
• Title: Domain Adapting Speech Emotion Recognition modals to real-world scenario with Deep Reinforcement Learning
• Authors: Thejan Rajapakshe, Rajib Rana, Sara Khalifa
• Abstract summary: Domain adaptation allows us to transfer knowledge learnt by a model across domains after a phase of training. We present a deep reinforcement learning-based strategy for adapting a pre-trained model to a newer domain.
• Score: 5.40755576668989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep reinforcement learning has been a popular training paradigm as deep learning has gained popularity in the field of machine learning. Domain adaptation allows us to transfer knowledge learnt by a model across domains after a phase of training. The inability to adapt an existing model to a real-world domain is one of the shortcomings of current domain adaptation algorithms. We present a deep reinforcement learning-based strategy for adapting a pre-trained model to a newer domain while interacting with the environment and collecting continual feedback. This method was used on the Speech Emotion Recognition task, which included both cross-corpus and cross-language domain adaption schema. Furthermore, it demonstrates that in a real-world environment, our approach outperforms the supervised learning strategy by 42% and 20% in cross-corpus and cross-language schema, respectively.

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