Abstract
This paper contains a PhD research proposal related to the domain of automatic emotion recognition from speech signal. We started by identifying our research problem, namely the acute confusion problem between emotion classes and we have cited different sources of this ambiguity. In the methodology section, we presented a method based on simililarity concept between a class and an instance patterns. We dubbed this method as Weighted Ordered classes – Nearest Neighbors. The first result obtained exceeds in performance the best result of the state-of-the art. Finally, as future work, we have made a proposition to improve the performance of the proposed system.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Li, W., Zhang, Y., Fu, Y.: Speech emotion recognition in E-learning system based on affective computing. In: Proceedings - Third International Conference on Natural Computation, ICNC, Hainan, China, pp. 809–813 (2007)
Jones, C.M., Jonsson, I.-M.: Performance analysis of acoustic emotion recognition for in-car conversational interfaces. In: Stephanidis, C. (ed.) UAHCI 2007 (Part II). LNCS, vol. 4555, pp. 411–420. Springer, Heidelberg (2007)
Clavel, C., et al.: De la construction du corpus émotionnel au système de détection le point de vue applicatif de la surveillance dans les lieux publics. Revue d’Intelligence Artificielle 20(4-5), 529–551 (2006)
Inanoglu, Z., Caneel, R.: Emotive alert: HMM-based emotion detection in voicemail messages. In: International Conference on Intelligent User Interfaces, Proceedings IUI, San Diego, CA, United States, pp. 251–253 (2005)
Panat, A.R., Ingole, V.T.: Affective state analysis of speech for speaker verification: Experimental study, design and development. In: Proceedings - International Conference on Computational Intelligence and Multimedia Applications, India, pp. 255–261 (2007)
Devillers, L., Vidrascu, L.: Real-life emotion recognition in speech. In: Müller, C. (ed.) Speaker Classifcation II. LNCS (LNAI), vol. 4441, pp. 34–42. Springer, Heidelberg (2007)
Lee, C.M., Narayanan, S.: Emotion Recognition Using a Data-Driven Fuzzy Inference System. In: Eurospeech, Geneva (2003)
Banse, R., Scherer, K.R.: Acoustic Profiles in Vocal Emotion Expression. Journal of Personality and Social Psychology, 614–636 (1996)
Ververidis, D., Kotropolos, C.: Automatic speech classification to five emotional states based on gender information. In: Proc. of Eusipco, pp. 341–344 (2004)
Scherer, K.R.: Vocal communication of emotion: A review of research paradigms. Speech Communication 40(1-2), 227–256 (2003)
Sethu, V., Ambikairajah, E., Epps, J.: Speaker normalisation for speech-based emotion detection. In: 15th International Conference on Digital Signal Processing, Cardiff, UK, pp. 611–614 (2007)
Grimm, M., Kroschel, K.: Rule-based emotion classification using acoustic features. In Proc. 3rd Internat. Conf. on Telemedicine and Multimedia Communication. Kajetany, Poland (2005)
Lee, C.M., et al.: Emotion Recognition based on Phoneme Classes. In: ICSLP, Korea (2004)
Schuller, B., et al.: Comparing one and two-stage acoustic modeling in the recognition of emotion in speech. In: IEEE Workshop on Automatic Speech Recognition and Understanding, Japan (2007)
Schuller, B., et al.: Towards more reality in the recognition of emotional. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Honolulu, HI, USA, pp. 941–944 (2007)
Attabi, Y.: Reconnaissance automatique des émotions à partir du signal acoustique, Master Thesis, École de technologie supérieure (ÉTS), Montréal (2009)
Huang, R., Ma, C.: Toward a speaker-independent real-time affect detection system. In: 18th International Conference on Pattern Recognition, Hong Kong, China (2006)
Chen, C., You, M., Song, M., Bu, J., Liu, J.: An enhanced speech emotion recognition system based on discourse information. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3991, pp. 449–456. Springer, Heidelberg (2006)
Lee, C.M., Narayanan, S.: Emotion Recognition Using a Data-Driven Fuzzy Inference System. In: Eurospeech, Geneva (2003)
Schuller, B., Steidl, S., Batliner, A.: The Interspeech 2009 Emotion Challenge. In: Interspeech. ISCA, Brighton (2009)
Lee, C., Mower, E., Busso, C., Lee, S., Narayanan, S.: Emotion Recognition Using a Hierarchical Binary Decision Tree Approach. In: Interspeech, ISCA, Brighton (2009)
Kockmann, M., Burget, L., Černocký, J.: Brno University of Technology System for Interspeech 2009 Emotion Challenge. In: Interspeech. ISCA, Brighton (2009)
Attabi, Y., Dumouchel, P.: Weighted Ordered Classes - Nearest Neighbors: A New Framework for Automatic Emotion Recognition From Speech. In: Interspeech. ISCA, Florence (2011)
Dehak, N., Kenny, et al.: Front-End Factor Analysis for Speaker Verification Submitted to IEEE Transactions on Audio, Speech and Language Processing (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Attabi, Y., Dumouchel, P. (2011). Automatic Emotion Recognition from Speech A PhD Research Proposal. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-24571-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24570-1
Online ISBN: 978-3-642-24571-8
eBook Packages: Computer ScienceComputer Science (R0)