Abstract
This paper describes continuation of our research on automatic emotion recognition from speech based on Gaussian Mixture Models (GMM). We use similar technique for emotion recognition as for speaker recognition. From previous research it seems to be better to use a lesser number of GMM components than is used for speaker recognition and better results are also achieved for a greater number of speech parameters used for GMM modeling. In previous experiments we used suprasegmental and segmental parameters separately and also together, which can be described as fusion on feature level. The experiment described in this paper is based on an evaluation of score level fusion for two GMM classifiers used separately for segmental and suprasegmental parameters. We evaluate two techniques of score level fusion – dot product of scores from both classifiers and maximum selection and maximum confidence selections.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dellaert, F., Polzin, T., Waibel, A.: Recognizing Emotion in Speech. In: The Fourth International Conference on Spoken Language Processing ICSLP 1996, Philadelphia, pp. 1970–1973 (1996)
Morrison, D., Wang, R., De Silva, L.C.: Ensemble methods for spoken emotion recognition in call-centers. Speech Communication 49, 98–112 (2007)
Truong, K.P., Leeuven, D.A.: An ‘open-set’ detection evaluation methology for automatic emotion recognition in speech. In: ParaLing 2007: Workshop on Paralinguistic Speech - between models and data, Saarbrücken, Germany (2007)
Nwe, T.L., Foo, S.W., DeSilva, L.C.: Speech emotion recognition using hidden Markov models. Speech Communication 41, 603–623 (2003)
Vondra, M., Vích, R.: Recognition of Emotions in German Speech using Gaussian Mixture Models. In: Esposito, A., Hussain, A., Marinaro, M., Martone, R. (eds.) Multimodal Signals 2008. LNCS, vol. 5398, pp. 256–263. Springer, Heidelberg (2008)
Vondra, M., Vích, R.: Evaluation of Automatic Speech Emotion Recognition Based on Gaussian Mixture Models. In: Proc. 19. Konferenz Elektronische Sprachsignalverarbeitung, Frankfurt am Main, September 8-10, pp. 172–176 (2008)
Vích, R., Vondra, M.: Experimente mit dem Teager Energie Operator. In: Proc. 19. Konferenz Elektronische Sprachsignalverarbeitung, Frankfurt am Main, September 8-10, pp. 29–36 (2008)
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A Database of German Emotional Speech. In: Proc. Interspeech 2005, Lisbon, Portugal, September 4-8 (2005)
Sjölander, K., Beskow, J.: Wavesurfer, http://www.speech.kth.se/wavesurfer/
Brookes, M.: VOICEBOX: Speech Processing Toolbox for MATLAB, http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html
Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models. Speech Communication 17, 91–108 (1995)
Kinnunen, T., Hautamäki, V., Fränti, P.: On the fusion of dissimilarity- based classifiers for speaker identification. In: Proc. 8th European Conference on Speech Communication and Technology (Eurospeech 2003), Geneva, Switzerland, pp. 2641–2644 (2003)
Shami, M., Verhelst, W.: An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech. Speech Communication 49, 201–212 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vondra, M., Vích, R. (2009). Evaluation of Speech Emotion Classification Based on GMM and Data Fusion. In: Esposito, A., Vích, R. (eds) Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Lecture Notes in Computer Science(), vol 5641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03320-9_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-03320-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03319-3
Online ISBN: 978-3-642-03320-9
eBook Packages: Computer ScienceComputer Science (R0)