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This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector Machines (SVMs), were used in experiments. SVMs turned out to provide the best classification accuracy of 75.44% in the speaker dependent mode, that is, when speech samples from the same speaker were included in the training corpus. Various speaker dependent and speaker independent configurations were analyzed and compared. Emotion recognition in speaker dependent conditions usually yielded higher accuracy results than a similar but speaker independent configuration. The improvement was especially well observed if the base recognition ratio of a given speaker was low. Happiness and anger, as well as boredom and neutrality, proved to be the pairs of emotions most often confused.
Rocznik
Tom
Strony
797--808
Opis fizyczny
Bibliogr. 41 poz., tab., wykr.
Twórcy
autor
- Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
autor
- Institute of Telecommunications, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Bibliografia
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- [2] Ayadi, M.E., Kamel, M.S. and Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases, Pattern Recognition 44(3): 572–587.
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- [12] Erden, M. and Arslan, L.M. (2011). Automatic detection of anger in human–human call center dialogs, Interspeech 2011, Florence, Italy, pp. 81–84.
- [13] Gajsek, R., Mihelic, F. and Dobrisek, S. (2013). Speaker state recognition using an HMM-based feature extraction method, Computer Speech and Language 27(1): 135–150.
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- [17] He, L., Lech, M., Memon, S. and Allen, N. (2008). Recognition of stress in speech using wavelet analysis and teager energy operator, Interspeech 2008, Brisbane, Australia, pp. 605–608.
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- [21] Janicki, A. and Turkot, M. (2008). Speaker emotion recognition with the use of support vector machines, Telecommunication Review and Telecommunication News (8–9): 994–1005, (in Polish).
- [22] Jeleń, Ł., Fevens, T. and Krzyżak, A. (2008). Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies, International Journal of Applied Mathematics and Computer Science 18(1): 75–83, DOI: 10.2478/v10006-008-0007-x.
- [23] Kaminska, D. and Pelikant, A. (2012). Recognition of human emotion from a speech signal based on Plutchik’s model, International Journal of Electronics and Telecommunications 58(2): 165–170.
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- [34] Scherer, K.R. (2003). Vocal communication of emotion: A review of research paradigms, Speech Communication 40(1–2): 227–256.
- [35] Schuller, B., Koehler, N., Moeller, R. and Rigoll, G. (2006). Recognition of interest in human conversational speech, Interspeech 2006, Pittsburgh, PA, USA, pp. 793–796.
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- [37] Seppi, D., Batliner, A., Schuller, B., Steidl, S., Vogt, T.,Wagner, J., Devillers, L., Vidrascu, L., Amir, N. and Aharonson, V. (2008). Patterns, prototypes, performance: Classifying emotional user states, Interspeech 2008, Brisbane, Australia, pp. 601–604.
- [38] Vapnik, V.N. (1982). Estimation of Dependences Based on Empirical Data, Springer-Verlag, New York, NY, (translation of Vosstanovlenie zavisimostei po empiricheskim dannym by Samuel Kotz).
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- [40] Yacoub, S., Simske, S., Lin, X. and Burns, J. (2003). Recognition of emotions in interactive voice response systems, Eurospeech 2003, Geneva, Switzerland, pp. 1–4.
- [41] Yu, C., Aoki, P. M. and Woodruff, A. (2004). Detecting user engagement in everyday conversations, 8th International Conference on Spoken Language Processing (ICSLP 2004), Jeju, Korea, pp. 1–6.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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