Audio-visual feature selection and reduction for emotion classification

International Conference on Auditory-Visual Speech Processing 2008, AVSP 2008

Volumn , Issue , 2008, Pages 185-190

  Haq, Sanaul ^a   Jackson, Philip J B ^a   Edge, James ^a  

^a UNIVERSITY OF SURREY   (United Kingdom)

Author keywords

Emotion recognition; Multimodal feature selection; Principal component analysis

Indexed keywords

DISCRIMINANT ANALYSIS; EMOTION RECOGNITION; FEATURE SELECTION; LINEAR TRANSFORMATIONS; SPEECH RECOGNITION;

AUDIO-VISUAL FEATURES; BHATTACHARYYA DISTANCE; EMOTION RECOGNITION; FEATURES SELECTION; LINEAR DISCRIMINANT ANALYZE; MULTIMODAL FEATURE SELECTION; MULTIMODAL FEATURES; PRINCIPAL COMPONENT ANALYSIS-DISCRIMINANT ANALYSIS; PRINCIPAL-COMPONENT ANALYSIS; VISUAL FEATURE;

PRINCIPAL COMPONENT ANALYSIS;

EID: 85133493946     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Ortony, A. and Turner, T.J., “What’s Basic About Basic Emotions?”, Psychological Review, 97(3):315-331, 1990.
2. Scherer, K.R., “What are emotions? And how can they be measured?”, Social Science Information, 44(4):695-729, 2005.
3. Russell, J.A., Ward, L.M. and Pratt, G., “Affective Quality Attributed to Environments: A Factor Analytic Study”, Environment and Behaviour, 13(3):259-288, 1981.
4. Douglas-Cowie, E., Cowie, R. and Schroeder, M., “A New Emotional Database: Considerations, Sources and Scope”, In Proc. of ISCA Workshop Speech and Emotion: A conceptual framework for research, Belfast, 39-44, 2000.
5. Burkhardt, F., et al., “A Database of German Emotional Speech”, In Proc. of Interspeech 2005, Lisbon, 1517-1520, 2005.
6. Amir, N., Ron, S. and Laor, N., “Analysis of emotional speech corpus in Hebrew based on objective criteria”, In Proc. of ISCA Workshop Speech and Emotion: A conceptual framework for research, Belfast, 29-33, 2000.
7. Luengo, I., Navas, E., et al., “Automatic Emotion Recognition using Prosodic Parameters”, In Proc. of Interspeech 2005, Lisbon, 493-496, 2005.
8. Ververidis, D. and Kotropoulos, C., “Emotional speech classification using Gaussian mixture models”, In Proc. of ISCAS 2005, Kobe, 2871-2874, 2005.
9. Vidrascu, L., et al., “Detection of real-life emotions in call centers”, In Proc. of Interspeech 2005, Lisbon, 1841-1844, 2005.
10. Borchert, M. and Düsterhöft, A., “Emotions in Speech - Experiments with Prosody and Quality Features in Speech for Use in Categorical and Dimensional Emotion Recognition Environments”, In Proc. of NLP-KE’05, Wuhan, 147-151, 2005.
11. Nogueiras, A., Moreno, A., et al., “Speech Emotion Recognition Using Hidden Markov Models”, In Proc. of Eurospeech 2001, Scandinavia, 2679-2682, 2001.
12. Lin, Y. and Wei, G., “Speech Emotion Recognition Based on HMM and SVM”, In Proc. of the 4th Int. Conf. on Mach. Learn. and Cybernetics, Guangzhou, 4898-4901, 2005.
13. Kao, Y. and Lee, L., “Feature Analysis for Emotion Recognition from Mandarin Speech Considering the Special Characteristics of Chinese Language”, In Proc. of Interspeech 2006, Pittsburgh, 1814-1817, 2006.
14. Neilberg, D., Elenius, K., et al., “Emotion Recognition in Spontaneous Speech Using GMMs”, In Proc. of Interspeech 2006, Pittsburgh, 809-812, 2006.
15. Chen, C.Y., et al., “Visual/Acoustic emotion recognition”, In Proc. of Int. Conf. on Multimedia and Expo, 1468-1471, 2005.
16. Busso, C., et al., “Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information”, In Proc. of the ACM Int. Conf. on Multimodal Interfaces, 205-211, 2004.
17. Song, M., Chen, C., and You, M., “Audio-visual based emotion recognition using tripled Hidden Markov Model”, In Proc. of Int. Conf. on ASSP, 5:877-880, 2004.
18. Busso, C., and Narayanan, S., “Interrelation Between Speech and Facial Gestures in Emotional Utterances: A Single Subject Study”, IEEE Transactions on ASLP, 2007.
19. Schuller, B., Rigoll, G. and Lang, M., “Hidden Markov Model-Based Speech Emotion Recognition”, In Proc. of ICASSP 2003, Hong Kong, 2:1-4, 2003.
20. Campbell, J.P., “Speaker Recognition: A Tutorial”, In Proc. of the IEEE, 85(9):1437-1462, 1997.
21. Huckvale, M., “Speech Filing System”, UCL Dept. of Phonetics & Linguistics, UK. Online: http://www.phon.ucl.ac.uk/resource/sfs/, accessed on 3 April 2008.
22. Young, S. and Woodland, P., “Hidden Markov Model Toolkit”, Cambridge University Engineering Department (CUED), UK. Online: http://htk.eng.cam.ac.uk/, accessed on 3 April 2008.
23. Chen, C.H., “Pattern Recognition and Signal Processing”, Sijthoff & Noordoff International Publishers, The Netherlands, 1978.
24. Shlens, J., “A Tutorial on Principal Component Analysis”, Systems Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, 2005.
25. Duda, R.O., Hart, P.E. and Stork, D.G., “Pattern Classification”, John Wiley & Sons, Inc. USA, Canada, 2001.
26. Schuller, B., Vlasenko, B., et al., “Comparing one and two-stage acoustic modeling in the recognition of emotion in speech”, IEEE Workshop on ASRU, 596-600, 2007.