RELEVANCE OF THE TYPES AND THE STATISTICAL PROPERTIES OF FEATURES IN THE RECOGNITION OF BASIC EMOTIONS IN SPEECH

Milana Bojanić, Vlado Delić, Milan Sečujski

DOI Number
-
First page
425
Last page
433

Abstract


Due to the advance of speech technologies and their increasing usage in various applications, automatic recognition of emotions in speech represents one of the emerging fields in human-computer interaction. This paper deals with several topics related to automatic emotional speech recognition, most notably with the improvement of recognition accuracy by lowering the dimensionality of the feature space and evaluation of the relevance of particular feature types. The research is focused on the classification of emotional speech into five basic emotional classes (anger, joy, fear, sadness and neutral speech) using a recorded corpus of emotional speech in Serbian.

Full Text:

PDF

References


D.A. Sauter, F. Eisner, P. Ekman, S. Scott, "Crosscultural recognition of basic emotions through non¬verbal emotional vocalizations", Proceedings of National Academy of Sciences of the USA, vol. 107(6), pp. 2408-2412, 2010.

D. Ververidis, C. Kotropoulos, "Emotional speech recognition: Resources, features and methods", Speech Communication, vol. 48, pp. 1162-1181, 2006.

S.L. Lutfi, F. Fernandez-Martinez, J.M. LucasCuesta, L. Lopez-Lebon, J.M. Montero, "A satisfaction-based model for affect recognition from conversational features in spoken dialog systems", Speech Communication, vol. 55, pp. 825-840, 2013.

M.E. Ayadi, M.S. Kamel, F. Karray, "Survey on speech emotion recognition: Features, classification schemes and databases", Pattern Recognition, vol. 44, pp. 572-587, 2011.

N. Fragopanagos, J.G. Taylor, "Emotion recognition in human-computer interaction", Neural Networks, vol 18, pp. 389-405, 2005.

B. Schuller, B. Vlasenko, F. Eyben, G. Rigoll, A. Wendemuth, "Acoustic emotion recognition: a benchmark comparison of performances", IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009, Italy, 2009, pp. 552-557.

V. Delić, M. Bojanić, M. Gnjatović, M. Sečujski, S.T. Jovičić, "Discrimination capability of prosodic and spectral features for emotional speech recognition", Electronics and Electrical Engineering, Kaunas Technologija, vol. 18, no. 9, pp. 51-54, 2012.

M. Bojanić, "Extraction and selection of feature set for automatic emotional speech recognition", Ph.D. dissertation, Dept. Elect. Eng., Faculty of Technical Sciences, University of Novi Sad, 2013.

B. Schüller, A. Batliner, S. Steidl, D. Seppi, "Recognising realistic emotions and affect in speech: state of the art and lessons learnt from the first challenge", Speech Communication, vol. 53, pp. 1062-1087, 2011.

C.M. Lee, S.S. Narayanan, "Toward detecting emotions in spoken dialogs", IEEE Transactions Speech Audio Processing, vol. 13, no. 2, pp. 293-303, 2005.

H. Altun, G. Polat, "New frameworks to boost feature selection algorithms in emotion detection for improved human computer interaction", LNCS, vol. 4729, Berlin-Heidelberg: Springer, pp. 533-541, 2007.

R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd edition. Wiley, New York, 2000.

S.T. Jovičić., Z. Kašić, M. Djordjević, M. Rajković, "Serbian emotional speech database: design, processing and evaluation", in Proceedings of International Conference on Speech and Computer (SPECOM’2004), St Peterburg, 2004, pp.77–81.

K. Fukunaga, Introduction to Statistical Pattern Recognition. Academic Press, 1990.

P. Pudil, J. Novovicova, J. Kittler, "Floating search methods in feature selection", Pattern Recognition Lett., vol. 15, pp. 1119-1125, 1994.

A. Batliner et al., "Whodunnit – Searching for the most important feature types signalling emotion-related user states in speech", Computer Speech and Language, vol. 25, pp. 4-28, 2011.


Refbacks

  • There are currently no refbacks.


ISSN: 0353-3670 (Print)

ISSN: 2217-5997 (Online)

COBISS.SR-ID 12826626