Automatic emotion classification vs. human perception: Comparing machine performance to the human benchmark

2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012

Volumn , Issue , 2012, Pages 1253-1258

  Esparza, Jose ^a   Scherer, Stefan ^a,c   Brechmann, Andre ^b   Schwenker, Friedhelm ^a  

^a UNIVERSITY OF ULM   (Germany)

^b LEIBNIZ INSTITUTE FOR NEUROBIOLOGY   (Germany)

^c USC Institute for Creative Technologies ^*  (United States)

Author keywords

automatic emotion classification; human benchmark; human perception of emotion

Indexed keywords

CLASSIFICATION RESULTS; CONFUSION MATRICES; DATA SETS; EMOTION CLASSIFICATION; FEATURE SETS; HUMAN BENCHMARK; HUMAN PERCEPTION; MACHINE PERFORMANCE; MULTI-CLASS SUPPORT VECTOR MACHINES; MULTI-CLASSIFIER; RECOGNITION PERFORMANCE;

BENCHMARKING; CLASSIFICATION (OF INFORMATION); INFORMATION SCIENCE; SIGNAL PROCESSING;

BEHAVIORAL RESEARCH;

EID: 84868554211     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSPA.2012.6310484     Document Type: Conference Paper

References (26)

1. M. WOllmer, B. Schuller, F. Eyben, and G. Rigoll, "Combining long short-term memory and dynamic bayesian networks for incremental emotion-sensitive artificial listening, " Journal of Selected Topics in Signal Processing, vol. 4, no. 5, pp. 867-881, 2010.
2. F. Schwenker, S. Scherer, Y. Magdi, and G. Palm, "The GMM-SVM supervector approach for the recognition of the emotional status from speech, " in 19th International Conference on Artificial Neural Networks 2009 Part I, C. Alippi, Ed., Berlin Heidelberg, 2009, vol. LNCS 5768, pp. 894-903, Springer.
3. B. Vlasenko, B. Schuller, A. Wendemuth, and G. Rigoll, "Frame vs. turn-level: Emotion recognition from speech considering static and dynamic processing, " in Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (ACIr07), Berlin, Heidelberg, 2007, pp. 139-147, Springer-Verlag.
4. J. Wagner, T. Vogt, and E. Andre, "A systematic comparison of different hmm designs for emotion recognition from acted and spontaneous speech, " in Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (A CII '07), Berlin, Heidelberg, 2007, pp. 114-125, Springer-Verlag.
5. M. A. Hall and L. A. Smith, "Practical feature subset selection for machine learning, " in Proceedings of the 21st Australian Computer Science Conference. 1998, pp. 181-191, Springer.
6. T. Vogt and E. Andre, "Comparing feature sets for acted and spontaneous speech in view of automatic emotion recognition, " in Proceedings of IEEE International Conference on Multimedia and Expo (ICME'05). 2005, pp. 474-477, IEEE.
7. B. Wendt and H. Scheich, " The "Magdeburger Prosodie Korpus"-a spoken language corpus for fMRI-studies, " in Speech Prosody 2002. 2002, pp. 699-701, SProSIG.
8. K. R. Scherer, T. Johnstone, and G. Klasmeyer, Handbook of Affective Sciences-Vocal expression of emotion, chapter 23, pp. 433-456, Affective Science. Oxford University Press, 2003.
9. F. Burkhardt, A. Paeschke, M. Rolfes, W. Sendlmeier, and B. Weiss, "A database of german emotional speech:' in Proceedings of Interspeech 2005. 2005, pp. 1517-1520, ISCA.
10. S. Scherer, M. Oubbati, F. Schwenker, and G. Palm, "Real-time emotion recognition from speech using echo state networks, " in Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition (ANNPR'08), Berlin, Heidelberg, 2008, pp. 205-216, Springer.
11. S. Scherer, F. Schwenker, and G. Palm, "Classifier fusion for emotion recognition from speech, " in 3rd lET International Conference on Intelligent Environments 2007 (IE07). Sept. 2007, pp. 152-155, IEEE.
12. B. Wendt, Analysen Emotionaler Prosodie, vol. 20 of Hallesche Schriften zur Sprechwissenschaft und P honetik, Peter Lang Intemationaler Verlag der Wissenschaften, 2007.
13. D. Li, I. K. Sethi, N. Dirnitrova, and T. McGee, "Classification of general audio data for contentbased retrieval, " Pattern Recognition Letters, vol. 22, no. 5, pp. 533-544, 2001.
14. L. R. Rabiner, Fundamentals of Speech Recognition, Prentice-Hall, 1993.
15. H. K. Maganti, S. Scherer, and G. Palm, "A novel feature for emotion recognition in voice based applications, " in Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (ACII'07). 2007, pp. 710-711, Springer.
16. H. Hermansky, "The modulation spectrum in automatic recognition of speech, " in Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding. 1997, pp. 140-147, IEEE.
17. S. Scherer, J. Kane, C. GobI, and F. Schwenker, "Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification, " IEEE Transactions on Audio, Speech and Language Processing, under review.
18. H. Hermansky and N. Morgan, "Rasta processing of speech, " IEEE Transactions on Speech and Audio Processing, special issue on Robust Speech Recognition, vol. 2, pp. 578-589, 1994.
19. H.R. Pfitzinger, S. Burger, and S. Heid, "Syllable detection in read and spontaneous speech, " in Proceedings of The 4th International Conference on Spoken Language Processing (ICSLP'96), Philadelphia, 1996, vol. 2, pp. 1261-1264.
20. T. H. Crystal and A. S. House, "Articulation rate and the duration of syllables and stress groups in connected speech, " Journal of T he Acoustical Society of America, vol. 88, pp. 101-112, 1990.
21. H. J. Cedergren and H. Perreault, "Speech rate and syllable timing in spontaneous speech, " in T hird International Conference on Spoken Language Processing (ICSLP 94). 1994, pp. 1087-1090, IEEE.
22. I. Mayergoyz, Mathematical Models of Hysteresis and their Applications, Springer, 2003.
23. M. Bicego, V. Murino, and M. Figueiredo, "Similarity-based clustering of sequences using hidden markov models, " in Machine Learning and Data Mining in Pattern Recognition, P. Pemer and A. Rosenfeld, Eds., vol. 2734, pp. 95-104. Springer, 2003.
24. K. R. Scherer, R. Banse, and H. G. Wallbott, "Emotion inferences from vocal expression correlate across languages and cultures, " Journal of CrossCultural P sychology, vol. 32, pp. 76-92, 2001.
25. R. Van Bezooyen, Characteristics and Recognizability of Vocal Expressions of Emotion, Foris Pubns US A, 1984.
26. K. R. Scherer, R. Banse, H. G. Wallbott, and T. Goldbeck, "Vocal cues in emotion encoding and decoding, " Motivation and Emotion, vol. 15, pp. 123-148, 1991.