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Reseach Article

Towards Detecting Emotions from Real Time Speech

Published on July 2016 by Dipti H. Kale, Chitra N. Takle, Shoeb S. Shaikh, Reshma Chaudhari
National Conference on Role of Engineers in National Building
Foundation of Computer Science USA
NCRENB2016 - Number 3
July 2016
Authors: Dipti H. Kale, Chitra N. Takle, Shoeb S. Shaikh, Reshma Chaudhari
c6ca9532-4734-45df-b9cf-765bc0aa4ae3

Dipti H. Kale, Chitra N. Takle, Shoeb S. Shaikh, Reshma Chaudhari . Towards Detecting Emotions from Real Time Speech. National Conference on Role of Engineers in National Building. NCRENB2016, 3 (July 2016), 16-19.

@article{
author = { Dipti H. Kale, Chitra N. Takle, Shoeb S. Shaikh, Reshma Chaudhari },
title = { Towards Detecting Emotions from Real Time Speech },
journal = { National Conference on Role of Engineers in National Building },
issue_date = { July 2016 },
volume = { NCRENB2016 },
number = { 3 },
month = { July },
year = { 2016 },
issn = 0975-8887,
pages = { 16-19 },
numpages = 4,
url = { /proceedings/ncrenb2016/number3/25567-4057/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Role of Engineers in National Building
%A Dipti H. Kale
%A Chitra N. Takle
%A Shoeb S. Shaikh
%A Reshma Chaudhari
%T Towards Detecting Emotions from Real Time Speech
%J National Conference on Role of Engineers in National Building
%@ 0975-8887
%V NCRENB2016
%N 3
%P 16-19
%D 2016
%I International Journal of Computer Applications
Abstract

Fundamental factor in communication of humans is nothing but Emotions. It would be ideal to have human emotions automatically recognized by machines, mainly for improving human machine interaction. Most of work in field of emotion recognition is done using recorded or offline database. Very selective research work is carried in real time high performance emotion recognition. In application of human computer interaction Real-time high performance emotion recognition is necessary for analyzing and responding to the user's emotions while he or she is interacting with an application. The proper choices of features and classifiers are important for a real-time high performance emotion recognition system. In this paper real time emotion recognition system is proposed, which extracts the emotions from real time speech based on extracting prosody, quality and dynamic features, classification of emotions using Multidimensional SVM and testing real time speech samples with training databases with emotional speech in 'Native Marathi' language has been presented

References
  1. K. S. Rao, S. G. Koolagudi, and R. R. Vempada, "Emotion recognition from speech using global and local prosodic features," Int. J. Speech Technol. , vol. 16, no. 2, pp. 143–160, Aug. 2012.
  2. D. Ververidis and C. Kotropoulos, "Emotional speech recognition: Resources, features, and methods," Speech Commun. , vol. 48, no. 9, pp. 1162–1181, Sep. 2006.
  3. I. Luengo and E. Navas, "Automatic Emotion Recognition using Prosodic Parameters" pp. 493–496, 2005.
  4. C. M. Lee, S. Member, S. S. Narayanan, and S. Member, "Toward Detecting Emotions in Spoken Dialogs," vol. 13, no. 2, pp. 293–303, 2005.
  5. M. Borchert and a. Dusterhoft, "Emotions in speech - experiments with prosody and quality features in speech for use in categorical and dimensional emotion recognition environments," 2005 Int. Conf. Nat. Lang. Process. Knowl. Eng. , vol. 00, pp. 147–151, 2005.
  6. S. Wu, H. Tiago"Automatic Recognition Of Speech Emotion Using Long-Term Spectro-Temporal Features," 2009.
  7. A. B. Kandali, S. Member, A. Routray, and T. K. Basu, "Emotion recognition from Assamese speeches using MFCC features and GMM classifier. "
  8. Y. Pan, P. Shen, and L. Shen, "Speech Emotion Recognition Using Support Vector Machine," vol. 6, no. 2, pp. 101–108, 2012.
  9. M. Dumas, "Emotional Expression Recognition using Support Vector Machines. "
  10. P. Shen and X. Chen, "Automatic Speech Emotion Recognition Using Support Vector Machine," pp. 621–625, 2011.
  11. D. Fradkin and I. Muchnik, "Support Vector Machines for Classification," vol. 0000, pp. 1–9, 2006.
  12. A. Hassan and R. I. Damper, "Multi-class and hierarchical SVMs for emotion recognition. "
  13. N. Yang, R. Muraleedharan, J. Kohl, I. Demirkol, and W. Heinzelman, "Speech-based Emotion Classification using Multiclass SVM with Hybrid Kernel and Thresholding Fusion" pp. 455–460, 2012
Index Terms

Computer Science
Information Sciences

Keywords

Mfcc Prosody Features Quality Features Speech Emotion Recognition Support Vector Machine