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Reseach Article

Emotion based Speaker Recognition with Vector Quantization

Published on May 2014 by Shraddha Bhandavle, Rasika Inamdar, Aarti Bakshi
International Conference on Electronics & Computing Technologies
Foundation of Computer Science USA
ICONECT - Number 1
May 2014
Authors: Shraddha Bhandavle, Rasika Inamdar, Aarti Bakshi
d8c67a6a-bdb0-4294-813b-de0eb343059d

Shraddha Bhandavle, Rasika Inamdar, Aarti Bakshi . Emotion based Speaker Recognition with Vector Quantization. International Conference on Electronics & Computing Technologies. ICONECT, 1 (May 2014), 9-12.

@article{
author = { Shraddha Bhandavle, Rasika Inamdar, Aarti Bakshi },
title = { Emotion based Speaker Recognition with Vector Quantization },
journal = { International Conference on Electronics & Computing Technologies },
issue_date = { May 2014 },
volume = { ICONECT },
number = { 1 },
month = { May },
year = { 2014 },
issn = 0975-8887,
pages = { 9-12 },
numpages = 4,
url = { /proceedings/iconect/number1/16475-1408/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Electronics & Computing Technologies
%A Shraddha Bhandavle
%A Rasika Inamdar
%A Aarti Bakshi
%T Emotion based Speaker Recognition with Vector Quantization
%J International Conference on Electronics & Computing Technologies
%@ 0975-8887
%V ICONECT
%N 1
%P 9-12
%D 2014
%I International Journal of Computer Applications
Abstract

Speech is a most popular biometrics nowadays used for human interaction. An emotion is a mental and a physiological state of a person. Emotion Based Speaker Recognition has attracted many researchers. Emotions are associated with the variety of feelings and thoughts. An emotion based speaker recognition system, recognizes the person's emotionsbased on pitch, speaking style, intensity, sampling frequency. Mel frequency CepstralCoefficient is the first step in a speaker recognition system. In this paper, we are implementing the gender - based modified MFCC approach to differentiate the individuals. For the classification purpose we have used the K-means algorithm.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Emotionrecognition From Speech fourier Transform Traditional Mfcc Modern Mfccapproach nearest Neighboralgorithm K-means Vector Quantization.