The GMM-SVM supervector approach for the recognition of the emotional status from speech

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

Volumn 5768 LNCS, Issue PART 1, 2009, Pages 894-903

  Schwenker, Friedhelm ^a   Scherer, Stefan ^a   Magdi, Yasmine M ^b   Palm, Günther ^a  

^a UNIVERSITY OF ULM   (Germany)

^b Faculty of Media Engineering and Technology   (Egypt)

Author keywords

[No Author keywords available]

Indexed keywords

CALL CENTERS; CLASSIFIER SYSTEMS; EMOTION RECOGNITION; EMOTIONAL DATABASE; FEATURE SPACE; FEATURE VECTORS; GAUSSIAN MIXTURE MODEL; HUMAN-MACHINE; MEAN VECTOR; PERCEPTUAL LINEAR PREDICTIONS; RECOGNITION RATES; SPEECH SIGNALS; SUPERVECTOR; UNIVERSAL BACKGROUND MODEL;

BACKPROPAGATION; CLASSIFIERS; FACE RECOGNITION; MAGNETOSTRICTIVE DEVICES; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

SPEECH RECOGNITION;

EID: 70350580828     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04274-4_92     Document Type: Conference Paper

References (24)

1. Bilmes, J.A.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical report, International Computer Science Institute and Computer Science Division, Department of Electrical Engineering and Computer Science, U.C. Berkeley (1998)
2. Bimbot, F., Bonastre, J.-F., Fredouille, C., Gravier, G., Magrin-Chagnolleau, I., Meignier, S., Merlin, T., Ortega-Garcia, J., Petrovska-Delacrètaz, D., Reynolds,
3. D.A.: A Tutorial on Text-Independent Speaker Verification. EURASIP Journal on Applied Signal Processing, 430-451 (2004)
4. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A database of german emotional speech. In: Proceedings of Interspeech 2005 (2005)
5. Campbell, W.M., Sturim, D.E., Reynolds, D.A.: Support vector machines using gmm supervectors for speaker verification support vector machines using gmm supervectors for speaker verification. IEEE Signal Processing Letters 13(5), 308-311 (2006)
6. Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18(1), 32-80 (2001)
7. Dellaert, F., Polzin, T., Waibel, A.: Recognizing emotion in speech. In: Proceedings of ICSLP, pp. 1970-1973 (1996)
8. Devillers, L., Vidrascu, L., Lamel, L.: Challanges in real-life emotion annotation and machine learning based detection. Neural Networks 18, 407-422 (2005)
9. Drullman, R., Festen, J., Plomp, R.: Effect of reducing slow temporal modulations on speech reception. Journal of the Acousic Society 95, 2670-2680 (1994)
10. Fragopanagos, N., Taylor, J.G.: Emotion recognition in human-computer interaction. Neural Networks 18, 389-405 (2005)
11. Hermansky, H.: Auditory modeling in automatic recognition of speech. In: Proceedings of Keele Workshop (1996)
12. Hermansky, H.: The modulation spectrum in automatic recognition of speech. In: Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding (1997)
13. Hermansky, H., Morgan, N.: Rasta processing of speech. IEEE transactions on speech and audio processing 2(4) (October 1994)
14. Kandovan, R.S., Lashkari, M.R.K., Etemad, Y.: Optimization of speaker verification using adapted GaSPPR 2007ussian mixture models for high quality databases. In: Proceedings of the Fourth conference on IASTED International Conference, Anaheim, CA, USA, pp. 264-268. ACTA Press (2007)
15. Kanederaa, N., Araib, T., Hermansky, H., Pavele, M.: On the relative importance of various components of the modulation spectrum for automatic speech recognition. Speech Communications 28, 43-55 (1999)
16. Lee, C.M., Yildirim, S., Bulut, M., Kazemzadeh, A., Busso, C., Deng, Z., Lee, S., Narayanan, S.S.: Emotion recognition based on phoneme classes. In: Proceedings of ICSLP 2004 (2004)
17. Nicholson, J., Takahashi, K., Nakatsu, R.: Emotion recognition in speech using neural networks. Neural Computing and Applications 9, 290-296 (2000)
18. Oudeyer, P.-Y.: The production and recognition of emotions in speech: features and algorithms. International Journal of Human Computer Interaction 59(1-2), 157-183 (2003)
19. Platt, J.C.: Sequential Minimal Optimizer: A Fast Algorithm for Training Support Vector Machines. Technical report, Microsoft Research (April 1998)
20. Scheirer, E., Slaney, M.: Construction and evaluation of a robust multifeature speech/music discriminator. In: Proceedings of ICASSP, vol. 1, pp. 1331-1334 (1997)
21. Scherer, K.R., Johnstone, T., Klasmeyer, G.: Affective Science. In: Handbook of Affective Sciences - Vocal expression of emotion, vol. 23, pp. 433-456. Oxford University Press, Oxford (2003)
22. Scherer, S., Schwenker, F., Palm, G.: Classifier fusion for emotion recognition from speech. In: Proceedings of Intelligent Environments 2007, pp. 152-155 (2007)
23. Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
24. Yacoub, S., Simske, S., Lin, X., Burns, J.: Recognition of emotions in interactive voice response systems. In: Proceedings of Eurospeech 2003 (2003)