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arXiv · 2207.12248

Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition

Thejan Rajapakshe·Rajib Rana·Sara Khalifa·Bjorn W. Schuller
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Abstract

Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the shortcomings of SER methods. To address this challenge, researchers have developed domain adaptation techniques that transfer knowledge learnt by a model across the domain. Although existing domain adaptation techniques have improved performances across domains, they can be improved to adapt to a real-world live data feed situation where a model can self-tune while deployed. In this paper, we present a deep reinforcement learning-based strategy (RL-DA) for adapting a pre-trained model to a real-world live data feed setting while interacting with the environment and collecting continual feedback. RL-DA is evaluated on SER tasks, including cross-corpus and cross-language domain adaption schema. Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively.

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BibTeXRIS

Thejan Rajapakshe, Rajib Rana, Sara Khalifa, Bjorn W. Schuller. 2022-07-07. Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition. https://doi.org/10.1109/access.2024.3519761

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