Emotion perception and recognition from speech

Affective Information Processing

Volumn , Issue , 2009, Pages 93-110

  Wu, Chung Hsien ^a   Yeh, Jui Feng ^b   Chuang, Ze Jing ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHIAYI UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77950922687     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-1-84800-306-4_6     Document Type: Chapter

References (49)

1. Bhatti, M. W., Wang, Y., & Guan, L. (2004). A neural network approach for human emotion recognition in speech. IEEE International Symposium on Circuits and Systems, Vancouver, Canada (pp. 181-184).
2. Borod, J. C., & Madigan, N. K. (2000). Neuropsychology of emotion and emotional disorders: An overview and research directions. In J. C. Borod (Ed.), The Neuropsychology of Emotion (pp. 3-28). New York: Oxford University Press.
3. Breazeal, C., & Aryananda, L. (2002). Recognition of affective communicative intent in robotdirected speech. Autonomic Robots, 12, 83-104. (Pubitemid 34156624)
4. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. (Pubitemid 32933532)
5. Chuang, Z. J., & Wu, C. H. (2004). Multi-modal emotion recognition from speech and text. International Journal of Computational Linguistics and Chinese Language Processing, 9(2), 1-18
6. Cichosz, J., & Slot, K. (2005). Low-dimensional feature space derivation for emotion recognition. In Interspeech 2005, Lisbon, Portugal (pp. 477-480). (Pubitemid 43908103)
7. Cichosz, J., & Slot, K. (2007). Emotion recognition in speech signal using emotion-extracting binary decision trees. ACII 2007. http://www.di.uniba. it/intint/DC-ACII07/Chicosz.pdf
8. Cover, T. T., & Hart, P. E. (1967). Nearest neighbour pattern classification. IEEE Transactions on Information Theory, 13, 21-27.
9. Damasio, A. (1994). Descartes' error: Emotion, reason and the human brain. New York: Grosset/Putnam.
10. Deng, L., Droppo, J., & Acero, A. (2003). Recursive estimation of nonstationary noise using iterative stochastic approximation for robust speech recognition. IEEE Transactions on Speech and Au-dio, 11(6), 568-580.
11. Devillers, L., Vidrascu, L., & Lamel, L. (2005). Challenges in real-life emotion annotation and machine learning based detection. Neural Networks, 18, 407-422. (Pubitemid 40922648)
12. D'Mello, S. Picard, R. W. & Graesser, A. (2007). Towards an affect-sensitive autotutor. IEEE Intelligent Systems, Special issue on intelligent educational systems, 53-61.
13. Engberg, I. S., & Hansen, A. V. (1996). Documentation of the Danish emotional speech database (DES). Internal AAU Report, Center for Person Kommunikation, Denmark.
14. Fernandez, R., & Picard, R. W. (2005). Classical and novel discriminant features for affect recognition from speech. In Interspeech 2005, Lisbon, Portugal (pp. 473-476). (Pubitemid 43908102)
15. Fragopanagos, N. & Taylor, J. G. (2005). Emotion recognition in human-computer interaction. Neural Networks, 18(4), 389-405. (Pubitemid 40922647)
16. Huber, R., Noth, E., Batliner, A., Buckow, J., Warnke, V., & Niemann, H. (1998). You beep machine-emotion in automatic speech understanding systems. In Proceedings of the Workshop on Text, Speech, and Dialog. Masark University (pp. 223-228).
17. Inanoglu, Z., & Caneel, R. (2005). Emotive alertHMM-based emotion detection in voicemail messages. In IEEE Intelligent User Interfaces '05, San Diego (pp. 251-253).
18. Katz, G., Cohn, J., & Moore, C. (1996). A combination of vocal F0 dynamic and summary features discriminates between pragmatic categories of infant-directed speech. Child Development, 67, 205-217.
19. Kwon O., Chan K., Hao J., & Lee T. (2003). Emotion recognition by speech signals. In Proceedings of Eurospeech 2003, Geneva (pp. 125-128).
20. Lee, C.-H., Clements, M., Dusan, S., Fosler-Lussier, E., Johnson, K., Juang, B.-H., & Rabiner, L. (2007). An overview on automatic speech attribute transcription (ASAT). In Proceedings of Interspeech 2007, August 27-31, Antwerp, Belgium (pp. 1825-1828).
21. Lee, C. M., & Narayanan, S. S. (2005). Toward detecting emotions in spoken dialogs. IEEE Transactions on Speech and Audio Processing, 13(2), 293-303. (Pubitemid 40320247)
22. Levity, M., Huberz, R., Batlinery, A., & Noeth, E. (2001). Use of prosodic speech characteristics for automated detection of alcohol intoxication. In Prosody in Speech Recognition and Understanding, Molly Pitcher Inn, Red Bank, NJ.
23. Liscombe, J., Riccardi, G., & Hakkani-Tr, D. (2005). Using context to improve emotion detection in spoken dialogue systems. In Proceedings of Interspeech, Lisbon, Portugal (pp. 1845-1848). (Pubitemid 43908444)
24. Litman D., & Silliman, S. (2004). Itspoke. An intelligent tutoring spoken dialogue system. In Proceedings of the 4th Meeting of HLT/NAACL (Companion Proceedings), Boston, May (pp. 233-236).
25. Morrison, D., Wang, R., & De Silva, L. C. (2007). Ensemble methods for spoken emotion recognition in call-centres. Speech Communication, 49, 98-112.
26. Murray, I. R., & Arnott, J. L. (1993). Towards the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion. Journal of the Acoustic Society of America, 93(2), 1097-1108. (Pubitemid 23059837)
27. Nakatsu, R., Solomides, A., & Tosa, N. (1999). Emotion recognition and its application to computer agents with spontaneous interactive capabilities. In Proceedings of the IEEE International Conference on Multimedia Computing and Systems (ICMCS '99) (vol. 2; pp. 804-808).
28. Nwe, T., Foo, S., & De Silva, L. (2003). Speech emotion recognition using hidden Markov models. Speech Communications, 41(4), 603-623.
29. Ortony, A., & Turner, T. J. (1990). What's basic about basic emotions? Psychological Review, 97, 315-331.
30. Oudeyer, P. (2003). The production and recognition of emotions in speech: features and algorithms. International Journal of Human-Computer Studies, 59, 157-183.
31. Paeschke, A., & Sendlmeier, W. (2000). Prosodic characteristics of emotional speech: Measurements of fundamental frequency movements. In Proceedings of ISCA ITRW on Speech and Emotion, Belfast (pp. 75-80).
32. Pantic, M., & Rothkrantz, L. J. K. (2003). Toward an affect-sensitive multimodal human-computer interaction. Proceedings of the IEEE, 91(9), 1370-1390 (Pubitemid 40890819)
33. Parrott, W. (2001). Emotions in social psychology, Philadelphia: Psychology Press.
34. Petrushin, V. (2000). Emotion recognition in speech signal: Experimental study, development, and application. In Proceedings of the Sixth International Conference on Spoken Language Processing (ICSLP 2000), Beijing (pp. 222-225).
35. Petrushin, V. A. (1999). Emotion in speech recognition and application to call centers. In Proceedings of Artificial Neural Networks In Engineering (ANNIE 99) (pp. 7-10).
36. Picard, R. W., Vyzas, E., & Healey, J. (2001). Toward machine emotional intelligence: analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10), 1175-1191 (Pubitemid 33047955)
37. Pierre-Yves, O. (2003). The production and recognition of emotions in speech: Features and algorithms. International Journal of Human-Computer Studies, 59, 157-183
38. Rahurkar, M. A., & Hansen, J. H. L.(2003). Frequency distribution based weighted sub-band approach for classification of emotional/stressful content in speech. In Eighth European Conference on Speech Communication and Technology, Geneva (pp. 721-724).
39. Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. University of Chicago Press, Chicago.
40. Scherer, K. R. (1999). Appraisal theory. In T. Dalgleish, & M. Power (Eds.), Handbook of cognition and emotion (pp. 637-663). New York: John Wiley.
41. Shami, M., & Kamel, M. (2005). Segment-based approach to the recognition of emotions in speech. In IEEE Conference on Multimedia and Expo (ICME05), Amsterdam, The Netherlands. http://ieeexplore.ieee.org/iel5/10203/ 32544/01521436.pdf?tp=&isnumber=&arnumber=1521436
42. Shami, M., & Verhelst, W. (2007). An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech. Speech Communication 49, 201-212. (Pubitemid 46413361)
43. Shriberg, E. (2005). Spontaneous speech: How people really talk and why engineers should care. In Eurospeech 2005, Lisbon, Portugal.
44. Slaney, M., & McRoberts, G. (2003). A recognition system for affective vocalization. Speech Communication 39, 367-384.
45. Ten Bosch, L. (2003). Emotions, speech and the ASR framework. Speech Communication 40(1-2), 213-225.
46. Vapnik, V. (2005). The nature of statistical learning theory. New York: Springer-Verlag.
47. Ververidis, D., Kotropoulos, C., & Pitas, I. (2005). Automatic emotional speech classification. In IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal (pp. 593-596).
48. Wu, J., & Huo, Q. (2002). An environment compensated minimum classification error training approach and its evaluation on aurora2 database. In Seventh International Conference on Spoken Language, Denver (pp. 453-456).
49. Yacoub, S., Simske, S., Lin, X., & Burns, J. (2003). Recognition of emotion in interactive voice systems. In: Proceedings of Eurospeech 2003, Eighth European Conference on Speech Communication and Technology, Geneva (pp. 729-732).