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Comparative Analysis of Pitch and Formant for Recognizing Emotions of Isolated Marathi Speech

by Shaikh Nilofer R.A., R.R. Deshmukh, V.B. Waghmare
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 3
Year of Publication: 2015
Authors: Shaikh Nilofer R.A., R.R. Deshmukh, V.B. Waghmare
10.5120/ijca2015906495

Shaikh Nilofer R.A., R.R. Deshmukh, V.B. Waghmare . Comparative Analysis of Pitch and Formant for Recognizing Emotions of Isolated Marathi Speech. International Journal of Computer Applications. 128, 3 ( October 2015), 44-46. DOI=10.5120/ijca2015906495

@article{ 10.5120/ijca2015906495,
author = { Shaikh Nilofer R.A., R.R. Deshmukh, V.B. Waghmare },
title = { Comparative Analysis of Pitch and Formant for Recognizing Emotions of Isolated Marathi Speech },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 3 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 44-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number3/22856-2015906495/ },
doi = { 10.5120/ijca2015906495 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:20:29.889201+05:30
%A Shaikh Nilofer R.A.
%A R.R. Deshmukh
%A V.B. Waghmare
%T Comparative Analysis of Pitch and Formant for Recognizing Emotions of Isolated Marathi Speech
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 3
%P 44-46
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recognizing emotions from speech is a tuff task as we are not aware of the features which will accurately classify the emotions. This paper is an approach to show which speech feature classifies the emotions more accurately. The features compared here are Pitch and Formant while the classifier used is Linear Discriminant Analysis (LDA). The database used in this experiment was developed using 50 male and 50 female Marathi speaking native speakers. The emotions used here are Neutral, Happy, Sad, Surprise and Boredom. At the end of the experiment it was observed that formant recognized the emotions very efficiently and accurately with respect to that of energy.

References
  1. Reshma, Maninder, Amarbir Singh, “Speech emotion recognition by Gaussian mixture model”, International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 2969-2971
  2. L. R. Rabiner, M. J. Cheng, A. E. Rosenberg, and C. A. McGonegal. “A comparative performance study of several pitch detection algorithms”. IEEE Transactions on Audio, Signal, and Speech Processing 24, 399-417 1976.
  3. M. M. Sondhi, “New methods of pitch extraction,” IEEE Trans.Audio Electroacoust., vol. AU-16, pp. 262-266, June 1968.
  4. Yi Kechu, Tian Fu, Fu Qiang, “YU YIN XIN HAO CHU LI”, China Machine Press, Beijing, 2000
  5. Shashidhar G. Koolagudi · K. Sreenivasa Rao,” Emotion recognition from speech: a review”, Int J Speech Technol (2012) 15:99–117 DOI 10.1007/s10772-011-9125-1
  6. Chih-Hsien Huang, Cheng-Hung Tsai and Bo-Yi Li”The Corpus Preparation and Effective Feature Representation of Emotional Speech,” Fourth International Conference on Innovative Computing, Information and Control 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Linear Discriminant Analysis (LDA) Emotion Recognition Human Computer Interaction (HCI) Feature extraction.

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