An Interpretable Deep Mutual Information Curriculum Metric for a Robust and Generalized Speech Emotion Recognition System

Abstract

It is difficult to achieve robust and well-generalized models for tasks involving subjective concepts such as emotion. It is inevitable to deal with noisy labels, given the ambiguous nature of human perception. Methodologies relying on semi-supervised learning (SSL) and curriculum learning have been proposed to enhance the generalization of the models. This study proposes a novel deep mutual information (DeepMI) metric, built with the SSL pre-trained DeepEmoCluster framework to establish the difficulty of samples. The DeepMI metric quantifies the relationship between the acoustic patterns and emotional attributes (e.g., arousal, valence, and dominance). The DeepMI metric provides a better curriculum, achieving state-of-the-art performance that is higher than results obtained with existing curriculum metrics for speech emotion recognition (SER). We evaluate the proposed method with three emotional datasets in matched and mismatched testing conditions. The experimental evaluations systematically show that a model trained with the DeepMI metric not only obtains competitive generalization performances, but also maintains convergence stability. Furthermore, the extracted DeepMI values are highly interpretable, reflecting information ranks of the training samples.