Sequential Modeling by Leveraging Non-Uniform Distribution of Speech Emotion

The expression and perception of human emotions are not uniformly distributed over time. Therefore, tracking local changes of emotion within a segment can lead to better models for
speech emotion recognition
(SER), even when the task is to provide a sentence-level prediction of the emotional content. A challenge to exploring local emotional changes within a sentence is that most existing emotional corpora only provide sentence-level annotations (i.e., one label per sentence). This labeling approach is not appropriate for leveraging the dynamic emotional trends within a sentence. We propose a framework that splits a sentence into a fixed number of chunks, generating chunk-level emotional patterns. The approach relies on emotion rankers to unveil the emotional pattern within a sentence, creating continuous emotional curves. Our approach trains the sentence-level SER model with a
sequence-to-sequence
formulation by leveraging the retrieved emotional curves. The proposed method achieves the best
concordance correlation coefficient
(CCC) prediction performance for arousal (0.7120), valence (0.3125), and dominance (0.6324) on the MSP-Podcast corpus. In addition, we validate the approach with experiments on the IEMOCAP and MSP-IMPROV databases. We further compare the retrieved curves with time-continuous emotional traces. The evaluation demonstrates that these retrieved chunk-label curves can effectively capture emotional trends within a sentence, displaying a time-consistency property that is similar to time-continuous traces annotated by human listeners. The proposed SER model learns meaningful, complementary, local information that contributes to the improvement of sentence-level predictions of emotional attributes.