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

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.