Prediction of Emotion Change From Speech

Abstract

The fact that emotions are dynamic in nature and evolve across time has been explored relatively less often in automatic emotion recognition systems to date. Although within-utterance information about emotion changes recently has received some attention, there remain open questions unresolved, such as how to approach delta emotion ground truth, how to predict the extent of emotion change from speech, and how well change can be predicted relative to absolute emotion ratings. In this article, we investigate speech-based automatic systems for continuous prediction of the extent of emotion changes in arousal/valence. We propose the use of regression (smoothed) deltas as ground truth for emotion change, which yielded considerably higher inter-rater reliability than first-order deltas, a commonly used approach in previous research, and represent a more appropriate approach to derive annotations for emotion change research, findings which are applicable beyond speech-based systems. In addition, the first system design for continuous emotion change prediction from speech is explored. Experimental results under the Output-Associative Relevance Vector Machine framework interestingly show that changes in emotion ratings may be better predicted than absolute emotion ratings on the RECOLA database, achieving 0.74 vs 0.71 for arousal and 0.41 vs 0.37 for valence in concordance correlation coefficients. However, further work is needed to achieve effective emotion change prediction performances on the SEMAINE database, due to the large number of non-change frames in the absolute emotion ratings.