Emotion recognition based on speech plays an important role in Human Computer Interaction (HCI), which has motivated extensive recent investigation into this area. However, current research on emotion recognition is focused on recognizing emotion on a per-file basis and mostly does not provide insight into emotion changes. In this paper, we report on an initial investigation into detecting the instant of emotion change using Gaussian Mixture Models (GMM) based methods, either without or with prior knowledge of emotions: the Generalized Likelihood Ratio and Emotion Pair Likelihood Ratios, together with a novel normalization scheme to improve emotion change detection accuracy. Experimental results based on the IEMOCAP corpus are presented that demonstrate a promising baseline. Despite the challenging nature of the problem, this work provides a path towards systems that detect and understand emotion changes, and also presents very interesting questions for further investigation.
Bibliographic reference. Huang, Zhaocheng / Epps, Julien / Ambikairajah, Eliathamby (2015): "An investigation of emotion change detection from speech", In INTERSPEECH-2015, 1329-1333.