Detection of affective states from text and speech for real-time human-computer interaction

Human Factors

Volumn 54, Issue 4, 2012, Pages 530-545

  Calix, Ricardo A ^a   Javadpour, Leili ^a   Knapp, Gerald M ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

cognitive processes; human computer interaction; knowledge representation; language

Indexed keywords

AFFECTIVE STATE; ASSISTIVE SYSTEM; AUTOMATED SYSTEMS; COGNITIVE PROCESS; EMOTION DETECTION; EMOTIONAL STATE; FEATURE ANALYSIS; HUMAN COMMUNICATIONS; HUMAN EMOTION; HUMAN LANGUAGE; LANGUAGE; LOUISIANA STATE UNIVERSITY; MACHINE LEARNING CLASSIFICATION; NEGATIVE SENTENCES; PROSODIC FEATURES; SPECTRAL FEATURE; SPEECH FEATURES; STEPWISE APPROACH; TEXT FEATURE; UNIVERSITY OF ILLINOIS;

AUTOMATION; HUMAN COMPUTER INTERACTION; KNOWLEDGE REPRESENTATION; SOFTWARE AGENTS; THREE DIMENSIONAL COMPUTER GRAPHICS;

SPEECH RECOGNITION;

ARTICLE; COMPUTER INTERFACE; EMOTION; FEMALE; HUMAN; LANGUAGE; MALE; RECORDING; TEXT MESSAGING; THEORETICAL MODEL; UNITED STATES; VERBAL BEHAVIOR;

EMOTIONS; FEMALE; HUMANS; LANGUAGE; MALE; MODELS, THEORETICAL; TAPE RECORDING; TEXT MESSAGING; UNITED STATES; USER-COMPUTER INTERFACE; VERBAL BEHAVIOR;

EID: 84863978289     PISSN: 00187208     EISSN: 15478181     Source Type: Journal    
DOI: 10.1177/0018720811425922     Document Type: Review

References (34)

1. Akenine-Moller T., Haines E., Hoffman N. Real-time rendering. Wellesley, MA: A K Peters ; 2008:
2. AlmC. O. (2008). Affect in text and speech (Unpublished doctoral dissertation). University of Illinois at Urbana-Champaign.
3. BoersmaP.WeeninkD. (2005). Praat: Doing phonetics by computer. Retrieved from http://www.fon.hum.uva.nl/praat/
4. Burges C. J.. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery. 1998 ; 2: 121-167 (Pubitemid 128695475)
5. Busso C., Lee S., Narayanan S.. Analysis of emotionally salient aspects of fundamental frequency for emotion detection. IEEE Transactions on Audio, Speech, and Language Processing. 2009 ; 17 (4). 582-596
6. CalixR. (2011). Automated semantic understanding of human emotions in writing and speech (Unpublished doctoral dissertation). Louisiana State University, Baton Rouge.
7. Calix R., Knapp G... Paper presented at the 2nd ACM Multimedia Systems conference ; 2011 San Jose, CA ; 2011..
8. Calix R., Mallepudi S., Chen B., Knapp G.. Emotion recognition in text for 3D facial expression rendering. IEEE Transactions on Multimedia. 2010 ; 12: 544-551
9. Castellano G., Kessous L., Caridakis G... Paper presented at the Doctoral Consortium of the 2nd International Conference on Affective and Intelligent Interaction ; 2007 Lisbon, Portugal ; 2007..
10. ChangC.-C.LinC. (2001). LIBSVM: A library for support vector machines. Retrieved from http://www.csie.ntu.edu.tw/~cjlin/libsvm
11. Chawla N., Bowyer K., Hall L., Kegelmeyer W.. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research. 2002 ; 16: 341-378
12. Chuang Z., Wu CH. Multi-modal emotion recognition from speech and text. Computational Linguistics and Chinese Language Processing. 2004 ; 9 (2). 45-62
13. Cube Maps. (2010). Cubical environment maps. Retrieved from http://www.codemonsters.de/home/content.php?show=cubemaps
14. Fontaine J., Scherer K. R., Roesch E., Ellsworth P.. The world of emotions is not two-dimensional. Psychological Science. 2007 ; 18: 1050-1057
15. Hansen J. H. L., Bou-Ghazale S.. Getting started with SUSAS: A speech under simulated and actual stress database. EUROSPEECH-97: International Conference on Speech Communication and Technology ; 1997 Rhodes, Greece ; 1997. 1743.
16. Havasi C., Speer R., Alonso J... Paper presented at the 22nd Conference on Artificial Intelligence ; 2007 Vancouver, Canada ; 2007..
17. Jurafsky D., Martin J. Speech and language processing. 2008 ). Speech and language processing (2nd ed.). Upper Saddle River, NJ: Prentice Hall. Upper Saddle River, NJ: Prentice Hall ; 2008:
18. Klabbers E., Mishra T., van Santen J.. Analysis of affective speech recordings using the super-positional intonation model. Proceedings of the 6th ISCA Workshop on Speech Synthesis ; 2007 ; 2007. 339.
19. Larsen J., McGraw A.. Further evidence for mixed emotions. Journal of Personality and Social Psychology. 2011 ; 100: 1095-1110
20. Liu H., Lieberman H., Selker T.. A model of textual affect sensing using real-world knowledge. Proceedings of the 2003 International Conference on Intelligent User Interfaces ; 2003 ; 2003. 125.
21. LSU-NLP. (2011). LSU Semantic Analysis Lab. Retrieved from http://nlp.lsu.edu/
22. Luengo I., Navas E., Hernaez I.. Feature analysis and evaluation for automatic emotion identification in speech. IEEE Transactions on Multimedia. 2010 ; 12: 490-501
23. Matthews G., Jones D., Chamberlain A.. Refining the measurement of mood: The UWIST Mood Adjective Checklist. British Journal of Psychology. 1990 ; 81: 17-42
24. McGraw A., Warren C.. Benign violations: Making immoral behavior funny. Psychological Science. 2010 ; 20: 1-9
25. Moilanen K., Pulman S.. Sentiment composition. Proceedings of Recent Advances in Natural Language Processing. 2007 ;: 378-382
26. Osherenko A. Proceedings of Artificial Intelligence and Simulation of Behavior. London: Society for the Study of Artificial Intelligence and Simulation of Behaviour ; 2008: 41-44.
27. Sebe N., Cohen I., Huang T. Handbook of pattern recognition and computer vision. Hackensack, NJ: World Scientific ; 2005:
28. SinghP. (2005). EM-ONE: An architecture for reflective common sense thinking (Unpublished PhD thesis). Massachusetts Institute of Technology, Cambridge.
29. Tapus A., Mataric M., Scassellati B.. The grand challenges in socially assistive robotics. IEEE Robotics and Automation Magazine. 2007 ; 14: 35-42 (Pubitemid 46566616)
30. Tapus A., Tapus C., Mataric M.. User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intelligent Service Robotics. 2008 ; 1: 169-183
31. Tawari A., Trivedi M.. Speech emotion analysis: exploring the role of context. IEEE Transactions on Multimedia. 2010 ; 12: 502-509
32. Tokuhisa R., Inui K., Matsumoto Y.. Emotion classification using massive examples extracted from the web. Proceedings of the 22nd International Conference on Computational Linguistics ; 2008 ; 2008. 881.
33. Versley Y., Ponzetto S. P., Poesio M., Eidelman V., Jern A., Smith J., Yang X., Moschitti A... Paper presented at the Language Resources and Evaluation Conference ; 2008 Marrakech, Morocco ; 2008..
34. Witten I., Frank E. Data mining: Practical machine learning tools and techniques. 2005 ). Data mining: Practical machine learning tools and techniques (2nd ed.). San Francisco, CA: Morgan Kaufmann. San Francisco, CA: Morgan Kaufmann ; 2005: