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Reseach Article

Methodology for Gender Identification, Classification and Recognition of Human Age

Published on December 2015 by Shivaji J Chaudhari, Ramesh M. Kagalkar
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 2
December 2015
Authors: Shivaji J Chaudhari, Ramesh M. Kagalkar
a655d505-5eb6-4d51-a7ad-c1c77f876ab5

Shivaji J Chaudhari, Ramesh M. Kagalkar . Methodology for Gender Identification, Classification and Recognition of Human Age. National Conference on Advances in Computing. NCAC2015, 2 (December 2015), 5-10.

@article{
author = { Shivaji J Chaudhari, Ramesh M. Kagalkar },
title = { Methodology for Gender Identification, Classification and Recognition of Human Age },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 2 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 5-10 },
numpages = 6,
url = { /proceedings/ncac2015/number2/23362-5023/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Shivaji J Chaudhari
%A Ramesh M. Kagalkar
%T Methodology for Gender Identification, Classification and Recognition of Human Age
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 2
%P 5-10
%D 2015
%I International Journal of Computer Applications
Abstract

The human voice is comprised of sound made by a human being using the vocal cord for talking,singing, laughing, crying and shouting. It is particularly a piece of human sound creation inwhich the vocal cord is the essential sound source, which play an important role in the conversation. The applications of speech or voice processing technology play a crucial role in humancomputer interaction. The system improves gender identification, age group classification, ageand emotion recognition performance. The research work uses new and efficient methods forfeature extraction of speech or voice and classification of standard method on the various audiodatasets. Mel Frequency Cepstral Coefficients feature extraction and selection is performed tofind a more suitable feature set for building speaker models. The proposed system uses GaussianMixture Model is a supervector for system feature selection and feature modelling. SupportVector Machine classification and feature matching technique is used to classify the featurefor different age groups like child, teenage, young, adult and senior to increase the resultantperformance and accuracy. The database is created using the audio files for each age group ofspeaker and for each emotion as an input, performs feature extraction and identifies the gender,classify age group, recognize age and emotion.

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Index Terms

Computer Science
Information Sciences

Keywords

Mel Frequency Cepstral Coefficient (mfcc) Gaussian Mixture Model (gmm) support Vector Machine (svm) Expectation-maximization (em) Maximum A Posteriori (map) Hidden Markov Models (hmms) Suprasegmental Hidden Markov Models (sphmms) Interactive Voice Response System (ivrs).