TY - GEN
T1 - Semi-coupled hidden Markov model with state-based alignment strategy for audio-visual emotion recognition
AU - Lin, Jen Chun
AU - Wu, Chung-Hsien
AU - Wei, Wen Li
PY - 2011/10/27
Y1 - 2011/10/27
N2 - This paper presents an approach to bi-modal emotion recognition based on a semi-coupled hidden Markov model (SC-HMM). A simplified state-based bi-modal alignment strategy in SC-HMM is proposed to align the temporal relation of states between audio and visual streams. Based on this strategy, the proposed SC-HMM can alleviate the problem of data sparseness and achieve better statistical dependency between states of audio and visual HMMs in most real world scenarios. For performance evaluation, audio-visual signals with four emotional states (happy, neutral, angry and sad) were collected. Each of the invited seven subjects was asked to utter 30 types of sentences twice to generate emotional speech and facial expression for each emotion. Experimental results show the proposed bi-modal approach outperforms other fusion-based bi-modal emotion recognition methods.
AB - This paper presents an approach to bi-modal emotion recognition based on a semi-coupled hidden Markov model (SC-HMM). A simplified state-based bi-modal alignment strategy in SC-HMM is proposed to align the temporal relation of states between audio and visual streams. Based on this strategy, the proposed SC-HMM can alleviate the problem of data sparseness and achieve better statistical dependency between states of audio and visual HMMs in most real world scenarios. For performance evaluation, audio-visual signals with four emotional states (happy, neutral, angry and sad) were collected. Each of the invited seven subjects was asked to utter 30 types of sentences twice to generate emotional speech and facial expression for each emotion. Experimental results show the proposed bi-modal approach outperforms other fusion-based bi-modal emotion recognition methods.
UR - http://www.scopus.com/inward/record.url?scp=80054845386&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80054845386&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24600-5_22
DO - 10.1007/978-3-642-24600-5_22
M3 - Conference contribution
AN - SCOPUS:80054845386
SN - 9783642245992
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 185
EP - 194
BT - Affective Computing and Intelligent Interaction - 4th International Conference, ACII 2011, Proceedings
T2 - 4th International Conference on Affective Computing and Intelligent Interaction, ACII 2011
Y2 - 9 October 2011 through 12 October 2011
ER -