INTERSPEECH 2013
14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

Emotion Recognition of Conversational Affective Speech Using Temporal Course Modeling

Jen-Chun Lin, Chung-Hsien Wu, Wen-Li Wei

National Cheng Kung University, Taiwan

In a natural conversation, a complete emotional expression is typically composed of a complex temporal course representing temporal phases of onset, apex, and offset. In this study, subemotional states are defined to model the temporal course of an emotional expression in natural conversation. Hidden Markov Models (HMMs) are adopted to characterize the sub-emotional states; each represents one temporal phase. A sub-emotion language model, which considers the temporal transition between sub-emotional states (HMMs), is further constructed to provide a constraint on allowable temporal structures to determine an optimal emotional state. Experimental results show that the proposed approach yielded satisfactory results on the MHMC conversationbased affective speech corpus, and confirmed that considering the complex temporal structure in natural conversation is useful for improving the emotion recognition performance from speech.

Full Paper

Bibliographic reference.  Lin, Jen-Chun / Wu, Chung-Hsien / Wei, Wen-Li (2013): "Emotion recognition of conversational affective speech using temporal course modeling", In INTERSPEECH-2013, 1336-1340.