Abstract
In this paper, a speech emotion recognition system and its application for call-center system is proposed. In general, a speech captured by cellular-phone contains noises due to the mobile network and speaker environment. In order to minimize the effect of these noises and so improve the system performance, we employ a simple MA filter at the feature domain. Two pattern classification methods, k-NN and SVM with probability estimate, are compared to distinguish two emotional states- neutral and anger- for call-center application. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance and it promises the feasibility of the agent for mobile communication services.
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References
Dellaert, F., Polzin, T., Waibel, A.: Recognizing emotion in Speec. In: Proc. International Conf. on Spoken Language Processing, pp. 1970–1973 (1996)
Scherer, K.R.: Adding the affective dimension: A new look in speech analysis and synthesi. In: Proc. International Conf. on Spoken Language Processing, pp. 1808–1811 (1996)
Zhou, G., Hansen, J.H.L., Kaiser, J.F.: Nonlinear Feature Based Classification of Speech Under Stress. IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSINGÂ 9(3) (March 2001)
Yacoub, S., Simske, S., Lin, X., Burns, J.: Recognition of emotions in interactive voice response system. In: Eurospeech 2003 Proc. (2003)
Kostov, V., Fukuda, S.: Emotion in user interface, Voice Interaction system. IEEE Intl Conf. on systems, Man, Cybernetics Representation (2), 798–803 (2000)
Oriyama, T.M., Oazwa,: Emotion recognition and synthesis system on speech. In: IEEE Intl. Conference on Multimedia Computing and Systems, pp. 840–844 (1999)
Lee, C.M., Narayanan, S., Pieraccini, R.: Classifying emotions in human-machine spoken dialogs. In: ICME 2002 (2002)
Wu, T.-F., Lin, C.-J., Weng, R.C.: Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research (2004)
Gu, Li., Zahorian, S.A.: A new robust algorithm for isolated word end-point detection. In: ICASSP 2002, Orlando, USA (2002)
Noll, M.: Pitch determination of human speech by the harmonic product spectrum, the harmonic sum spectrum, and a maximum likelihood estimate. In: Proceedings of the Symposium on Computer Processing Communications, pp. 779–797 (1969)
Ross, M.J., Shaer, H.L., Cohen, A., Freudberg, R., Manley, H.J.: Average magnitude difference function pitch extractor. ASSP 22, 353–362 (1974)
Sun, X.: A pitch determination algorithm based on subharmonic-to harmonic ratio. In: ICSLP, pp. 676–679 (2000)
Liu, M., Wan, C.: A study on content-based classification retrieval of audio database. In: Proc. of the International Database Engineering & Applications Symposium, pp. 339–345 (2001)
Kang, B.-S.: A text-independent emotion recognition algorithm using speech signal, MS Thesis, Yonsei University (2000)
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Yoon, WJ., Park, KS. (2007). A Study of Emotion Recognition and Its Applications. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_43
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DOI: https://doi.org/10.1007/978-3-540-73729-2_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73728-5
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