Emotion recognition using a hierarchical binary decision tree approach

Speech Communication

Volumn 53, Issue 9-10, 2011, Pages 1162-1171

  Lee, Chi Chun ^a   Mower, Emily ^a   Busso, Carlos ^b   Lee, Sungbok ^a   Narayanan, Shrikanth ^a  

^a University of Southern California   (United States)

^b The University of Texas at Dallas   (United States)

Author keywords

Bayesian Logistic Regression; Emotion recognition; Hierarchical structure; Support Vector Machine

Indexed keywords

ACOUSTIC FEATURES; BASELINE MODELS; BINARY CLASSIFICATION; BINARY DECISION TREES; CLASSIFICATION FRAMEWORK; CLASSIFICATION TASKS; COMPUTATIONAL STRUCTURE; COMPUTATIONAL STUDIES; DATA SETS; EMOTION RECOGNITION; EMOTIONAL DATABASE; HIERARCHICAL APPROACH; HIERARCHICAL STRUCTURES; HUMAN COMMUNICATIONS; HUMAN DECISION MAKING; HUMAN MACHINE INTERFACE; LOGISTIC REGRESSIONS; MITIGATE ERRORS; PERFORMANCE MEASURE; REAL-WORLD APPLICATION; SPEECH UTTERANCE; STATE TRACKING; STRUCTURE MAPS; SUPPORT VECTOR;

DATABASE SYSTEMS; DECISION MAKING; DECISION TREES; PLANT EXTRACTS; SPEECH RECOGNITION; STRUCTURAL DESIGN; SUPPORT VECTOR MACHINES;

BEHAVIORAL RESEARCH;

EID: 79960847182     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2011.06.004     Document Type: Article

References (35)

1. A. Agresti Categorical Data Analysis Wiley Series in Probability and Mathematical Statistics 1990 Wiley New York
2. Albornoz, E.M.; Milone, D.H.; Rufiner, H.L.; 2011. Spoken emotion recognition using hierarchical classifiers. Comput. Speech Lang. 25 (3), in press.
3. Amir, N.; Mixdorff, H.; Ofer Amir, D.R.; Diamond, G.M.; Pfitzinger, H.R.; Levi-Isserlish, T.; Abramson, S.; 2010. Unresolved anger: Prosodic analysis and classification of speech from a therapeutic setting. In: Speech Prosody.
4. Black, M.; Chang, J.; Narayanan, S.; 2008. An empirical analysis of user uncertainty in problem-solving child-machine interactions: Linguistic analysis of spontaneous children speech. In: Proc. Workshop on Child, Computer and Interaction, Chania, Greece.
5. Black, M.; Katsamanis, N.; Lee, C.-C.; Lammert, A.C.; Baucom, B.R.; Christensen, A.; Georgiou, P.G.; Narayanan, S.; 2010. Automatic classification of married couples' behavior using audio features. In: Proc. Interspeech, Makuhari, Japan, 2010.
6. S. Brave, C. Nass, and K. Hutchinson Computers that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent Internat. J. Human-Comput. Stud. 62 2 2005 161 178 (Pubitemid 40206241)
7. L. Brody, and J. Hall Gender and Emotion in Context 2008 The Guilford Press
8. C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. Chang, S. Lee, and S. Narayanan IEMOCAP: Interactive emotional dyadic motion capture database J. Language Resour. Eval. 42 2008 335 359
9. C. Busso, S. Lee, and S. Narayanan Analysis of emotionally salient aspects of fundamental frequency for emotion detection IEEE Trans. Audio Speech Language Process. 17 4 2009 582 596
10. N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer Smote: Synthetic minority over-sampling technique J. Artif. Intell. Res. 16 2002 321 357 (Pubitemid 43057176)
11. Eyben, F.; Woellmer, M.; Schuller, B.; 2009. Speech and music interpretation by large-space extraction, Tech. rep.; Institute for Human-Machine Communication, Technische Universitaet Muenchen. .
12. A. Genkin, D.D. Lewis, and D. Madigan Large-scale Bayesian logistic regression for text categorization Technometrics 49 3 2007 291 304 (Pubitemid 47292487)
13. Hassan, A.; Damper, R.I.; 2010. Multi-class and hierarchical svms for emotion recognition. In: Proc. Interspeech, pp. 2354-2357.
14. Herm, O.; Schmitt, A.; Liscombe, J.; 2008. When calls go wrong: How to detect problematic calls based on log-files and emotions. In: Proc. Interspeech.
15. Hosmer, D.; Lemeshow, S.; 2000. Applied Logistic Regression, second ed.; Wiley Series in Probability and Statistics.
16. T. Kanda, T. Hirano, D. Eaton, and H. Ishiguro Interactive robots as social partners and peer tutors for children: A field trial Human-Comput. Interact. 19 1 2004 61 84
17. A. Kapoor, and R. Picard Multimodal affect recognition in learning environments Proc. 13th Annual ACM Internat. Conf. on Multimedia 2005 ACM New York, NY, USA 677 682
18. R. Lazarus Relational meaning and discrete emotions Appraisal Process. Emotion: Theor.; Methods, Res. 2001 37 67
19. C.M. Lee, and S.S. Narayanan Toward detecting emotions in spoken dialogs IEEE Trans. Speech Audio Process. 13 2 2005 293 303 (Pubitemid 40320247)
20. Lee, C.-C.; Mower, E.; Busso, C.; Lee, S.; Narayanan, S.; 2009. Emotion recognition using a hierarchical binary decision tree approach. In: Proc. Interspeech, 2009.
21. Lee, C.-C.; Black, M.; Katsamanis, A.; Lammert, A.; Baucom, B.; Christensen, A.; Georgiou, P.G.; Narayanan, S.; 2010. Quantification of prosodic entrainment in affective spontaneous spoken interactions of married couples. In: Proc. Interspeech, Makuhari, Japan, 2010.
22. Q.-R. Mao, and Y.-Z. Zhan A novel hierarchical speech emotion recognition method based on improved ddagsvm Comput. Sci. Inform. Systems 7 1 2010 211 222
23. Metallinou, A.; Lee, S.; Narayanan, S.; 2010. Decision level combination of multiple modalities for recognition and analysis of emotion expression. In: Proc. Internat. Conf. on Acoustics, Speech, and Signal Processing.
24. Metallinou, A.; Busso, C.; Lee, S.; Narayanan, S.; 2010. Visual emotion recognition using compact facial representation and viseme information. In: Proc. Internat. Conf. on Acoustics, Speech, and Signal Processing (ICASSP).
25. E. Mower, M.J. Mataric, and S.S. Narayanan A framework for automatic human emotion classification using emotional profiles IEEE Trans. Audio Speech Language Process. 19:5 2011 1057 1070
26. Pantic, M.; Sebe, N.; Cohn, J.; Huang, T.; 2005. Affective multimodal human-computer interaction. In: Proc. 13th Annual ACM Internat. Conf. on Multimedia, ACM New York, NY, USA, 2005, pp. 669-676.
27. H. Prendinger, J. Mori, and M. Ishizuka Using human physiology to evaluate subtle expressivity of a virtual quizmaster in a mathematical game Internat. J. Human-Comput. Stud. 62 2 2005 231 245 (Pubitemid 40206245)
28. Schuller, B.; Batliner, A.; Seppi, D.; Steidl, S.; Vogt, T.; Wagner, J.; Devillers, L.; Vidrascu, L.; Amir, N.; Kessous, L.; Aharonson, V.; 2007. The relevance of feature type for the automatic classification of emotional user states: Low level descriptors and functionals. In: Proc. Interspeech.
29. Schuller, B.; Steidl, S.; Batliner, A.; 2009. The interspeech 2009 emotion challenge. In: Proc. Interspeech, Brighton, UK.
30. Steidl, S.; 2009. Automatic classification of emotion-related user states in spontaneous children's speech, Logos, Verlag, Berlin.
31. V.N. Vapnik The Nature of Statistical Learning Theory 1995 Springer New York
32. H.L. Wanger Measuring performance in category judgment studies on nonverbal behavior J. Nonverbal Behav. 17 1 1993 3 28
33. Xiao, Z.; Dellandrea, E.; Dou, W.; Chen, L.; 2007. Automatic hierarchical classification of emotional speech. In: Proc. ISMW, pp. 291-296.
34. Yildirim, S.; Lee, C.M.; Lee, S.; Potamianos, A.; Narayanan, S.; 2005. Detecting politeness and frustration state of a child in a detecting politeness and frustration state of a child in a conversational computer game. In: Proc. Eurospeech, Lisbon, Portugal.
35. S. Yildirim, S. Narayanan, and A. Potamianos Detecting emotional state of a child in a conversational computer game Comput. Speech Language 25 1 2011 29 44 (Special Issue on Affective Speech)