A hierarchical static-dynamic framework for emotion classification

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 2372-2375

  Mower, Emily ^a   Narayanan, Shrikanth ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Audio Visual Emotion; Emotion Classification; Emotion Profiles; Emotion Representation

Indexed keywords

AUDIO-VISUAL; DISCRIMINATIVE FEATURES; DYNAMIC STRATEGIES; EMOTION CLASSIFICATION; EMOTION PROFILES; EMOTION REPRESENTATION;

AUDIO ACOUSTICS; ESTIMATION; SPEECH COMMUNICATION;

AUDIO SIGNAL PROCESSING;

EID: 80051607532     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5946960     Document Type: Conference Paper

References (14)

1. E. Mower, M. Matarić, and S. Narayanan, "A framework for automatic human emotion classification using emotion profiles," IEEE Trans. on Audio, Speech, and Language Processing, Accepted for Publication.
2. B. Schuller and L. Devillers, "Incremental acoustic valence recognition: an intercorpus perspective on features, matching, and performance in a gating paradigm," in InterSpeech, Makuhari, Japan, Sept. 2010, pp. 801-804.
3. M. Wöllmer, A. Metallinou, F. Eyben, B. Schuller, and S. Narayanan, "Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling," in InterSpeech, Makuhari, Japan, Sept. 2010, pp. 2362-2365.
4. I. Cohen, N. Sebe, A. Garg, L.S. Chen, and T.S. Huang, "Facial expression recognition from video sequences: Temporal and static modeling," Computer Vision and Image Understanding, vol. 91, no. 1-2, pp. 160-187, 2003.
5. C. Busso, M. Bulut, C.C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J.N. Chang, S. Lee, and S.S. Narayanan, "IEMOCAP: Interactive emotional dyadic motion capture database," Journal of Language Resources and Evaluation, pp. 335-359, Nov. 5 2008.
6. M. S. Bartlett, G. Littlewort, M. Frank, C. Lainscsek, I. Fasel, and J. Movellan, "Recognizing facial expression: Machine learning and application to spontaneous behavior," IEEE CVPR, vol. 2, pp. 568-573, 2005.
7. Y. L. Lin and G. Wei, "Speech emotion recognition based on HMM and SVM," Conf. on Machine Learning and Cybernetics, vol. 8, pp. 4898-4901, Aug. 2005.
8. E. Mower, M. Matarić, and S. Narayanan, "Robust representations for out-ofdomain emotions using emotion profiles," in SLT, Berkeley, California, Dec. 2010.
9. F. Eyben, M. Wöllmer, and B. Schuller, "openEAR - introducing the munich open-source emotion and affect recognition toolkit," in ACII, Amsterdam, The Netherlands, Sept. 2009.
10. C. Busso, S. Lee, and S. Narayanan, "Using neutral speech models for emotional speech analysis," in InterSpeech, Antwerp, Belgium, Aug. 2007, pp. 2225-2228.
11. N. Tsapatsoulis, A. Raouzaiou, S. Kollias, R. Cowie, and E. Douglas-Cowie, "Emotion Recognition and Synthesis Based on MPEG-4 FAP's," in MPEG-4 Facial Animation: The Standard, Implementation, and Applications, I. S. Pandzic and R. Forchheimer, Eds., chapter 9, pp. 141-167. JohnWiley & Sons, Ltd., 2002.
12. P. P. G. Boersma, "Praat, a system for doing phonetics by computer," Glot international, vol. 5, no. 9/10, pp. 341-345, 2002.
13. Y. Lu, I. Cohen, X. S. Zhou, and Q. Tian, "Feature selection using principal feature analysis," in Int. Conf. on Multimedia, New York, NY, USA, 2007, pp. 301-304.
14. J. C. Platt, "Fast training of support vector machines using sequential minimal optimization," in Advances in Kernel Methods. MIT press, 1999, pp. 185-208.