Speech emotion classification and public speaking skill assessment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6219 LNCS, Issue , 2010, Pages 151-162

  Pfister, Tomas ^a   Robinson, Peter ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; CROSS VALIDATION; EMOTIONAL STATE; MIND READING; NON-VERBAL FEATURES; NOVEL APPLICATIONS; OPTIMISATIONS; PUBLIC SPEAKING; REAL TIME PERFORMANCE; REAL-TIME INFERENCE; SKILL ASSESSMENT; SPEECH EMOTION CLASSIFICATION;

BEHAVIORAL RESEARCH; INFERENCE ENGINES;

CLASSIFIERS;

EID: 77958466875     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14715-9_15     Document Type: Conference Paper

References (16)

1. Scherer, K. R.: Vocal affect expression: A review and a model for future research. Psychological bulletin 99, 143-165 (1986)
2. Haynes, J. D., Rees, G.: Decoding mental states from brain activity in humans. Nature Reviews Neuroscience 7, 523-534 (2006)
3. Sobol Shikler, T.: Analysis of affective expression in speech. PhD thesis, Cambridge University (2007)
4. Baron-Cohen, S., Golan, O., Wheelwright, S., Hill, J. J.: Mind Reading: The Interactive Guide to Emotions. Jessica Kingsley Publishers, University of Cambridge (2004) ISBN 1 84310 214 5
5. Golan, O., Baron-Cohen, S., Wheelwright, S., Hill, J. J.: Systemizing empathy: Teaching adults with asperger syndrome and high functioning autism to recognize complex emotions using interactive multimedia. Development and Psychopathology 18, 589-615 (2006)
6. Sobol Shikler, T., Robinson, P.: Classification of complex information: Inference of co-occurring affective states from their expressions in speech. IEEE Transactions on Pattern Analysis and Machine Intelligence 99 (2009)
7. Hall, M. A., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: An update. SIGKDD Explorations 11 (2009)
8. Vapnik, V. N.: The nature of statistical learning theory. Springer, Heidelberg (1998)
9. Eyben, F., Wöllmer, M., Schuller, B.: openEAR - Introducing the Munich opensource emotion and affect recognition toolkit. In: Proc. 4th International HUMAINE Association Conference on Affective Computing and Intelligent Interaction 2009 (ACII 2009). IEEE, Amsterdam (September 2009)
10. Altun, H., Polat, G.: New frameworks to boost feature selection algorithms in emotion detection for improved human-computer interaction. In: Mele, F., Ramella, G., Santillo, S., Ventriglia, F. (eds.) BVAI 2007. LNCS, vol. 4729, pp. 533-541. Springer, Heidelberg (2007)
11. Hall, M. A., Smith, L. A.: Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. In: Florida Artificial Intelligence Symposium, pp. 235-239 (1999)
12. Qing-kun, L., Pei-wen, Q.: Model selection for SVM using mutative scale chaos optimization algorithm. Journal of Shanghai University 10, 531-534 (2006)
13. Pöyhönen, S., Arkkio, A., Jover, P., Hyötyniemi, H.: Coupling pairwise support vector machines for fault classification. Control Engineering Practice 13, 759-769 (2005)
14. Schuller, B., Steidl, S., Batliner, A.: The Interspeech 2009 emotion challenge. In: Interspeech, Brighton, UK (2009)
15. Schuller, B., Reiter, S., Müller, R., Al-Hames, M., Lang, M., Rigoll, G.: Speaker independent speech emotion recognition by ensemble classification. In: IEEE International Conference on Multimedia and Expo. (2005)
16. Lee, C., Mower, E., Busso, C., Lee, S., Narayanan, S.: Emotion recognition using a hierarchical binary decision tree approach. In: Interspeech, Brighton, UK (2009)