Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-416X
Online ISSN:
0976-4178


  About IJCLR
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)

 

 
International Journal of Computational Linguistics Research
 

 

Automatic Recognition of Emotions from Speech
Martin Gjoreski, Hristijan Gjoreski, Andrea Kulakov
Faculty of Computer Science and Engineering Rugjer Boshkovikj 16, 1000 Skopje, Macedonia, Department of Intelligent Systems, Jozef Stefan Institute Jamova cesta 39, 1000 Ljubljana, Slovenia
Abstract: This paper presents an approach to recognition of human emotions from speech. Seven emotions are recognized: anger, fear, sadness, happiness, boredom, disgust and neutral. The approach is applied on a speech database, which consists of simulated and annotated utterances. First, numerical features are extracted from the sound database by using audio feature extractor. Next, the extracted features are standardized. Then, feature selection methods are used to select the most relevant features. Finally, a classification model is trained to recognize the emotions. Three classification algorithms are tested, with SVM yielding the highest accuracy of 89% and 82% using the 10 fold cross-validation and Leave-One- Speaker-Out techniques, respectively. “Sadness” is the emotion which is recognized with highest accuracy.
Keywords: Support Vector Machines, SVM, Classification Algorithms, Emotions, Automatic Recognition Automatic Recognition of Emotions from Speech
DOI:https://doi.org/10.6025/jcl/2019/10/4/101-107
Full_Text   PDF 232 KB   Download:   137  times
References:

[1] Myers, D. G. (2004). Theories of Emotion. Psychology: Seventh Edition. New York NY: Worth Publishers.
[2] Perez-Rosas, V., Mihalcea, R. (2013). Sentiment Analysis of Online Spoken Reviews. Interspeech.
[3] Halder, A., Konar, A., R. Mandal, A. (2013). Chakraborty. General and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition. IEEE Transactions on Systems, Man, and Cybernetics,43 (3) 2013.
[4] Horlings, R., Datcu, D., Rothkrantz, L. J. M. (2008). Emotion recognition using brain activity. Proceeding CompSysTech ’08 In: Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing.
[5] Metallinou, A., Lee, S., Narayanan, S. (2008). Audio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice. Multimedia. 2008. ISM 2008. IEEE International Symposium on Multimedia.
[6] Ekman, P. (1982). Emotions in the Human Faces.
[7] James, A. Russell. (1980). A circumplex model of affect.
[8] Juslin, P. N., Scherer, K. R. (2004). Vocal expression of affect. In J. A. Harrigan, R. Rosenthal, K. R. Scherer (Eds.). The new handbook of methods in nonverbal behavior research, 65-135, 2004.
[9] Scherer, K. R. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication 40. 227–256.
[10] Mena, M. E. (2012). Emotion Recognition From Speech Signals.
[11] Ververidis, D., Kotropoulos, C. (2003). A review of emotional speech databases. In: PCI 2003. 9th Panhellenic Conference on Informatics., 560–574, 2003.
[12] Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B. (2005). A Database of German Emotional Speech. In: Proc. Interspeech. p. 1517–1520.
[13] Eyben, F., Wöllmer, M., Schuller, B. (2010). OpenSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor.
[14] Eyben, F., Weninger, F., Wollmer, M. (2013). Bjorn Schuller. openSmile Documentation. Version 2.0.0.
[15] Deng, H., Runger, G., Tuv. E. (2011). Bias of importance measures for multi-valued attributes and solutions. In: Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN2011).
[16] I. Kononenko, E. Simec, M. Robnik-Sikonja. Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence, Forthcoming.
[17] Demšar, J., Zupan, B. Orange: From experimental machine learning to interactive data mining. White Paper (http://www.ailab.si/orange). Faculty of Computer and Information Science. University of Ljubljana.
[18] Aha, D., Kibler, D. (1991). Instance-based learning algorithms. 1991, Machine Learning. 6, 37-66.
[19] Cristianini, N., Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press.
[20] Stuart, R., Peter, N. Artificial Intelligence: A Modern Approach. Second Edition, Prentice Hall.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright 2011 dline.info