Age and gender recognition of a speaker from short-duration phone conversations

Author

Yucesoy, Ergun ; Nabiyev, Vasif V.

Author_Institution

Teknik Bilimler Meslek Yuksekokulu, Ordu Univ., Ordu, Turkey

fYear

2015

fDate

16-19 May 2015

Firstpage

751

Lastpage

754

Abstract

In this study, a system is suggested that is classifying phone conversations having an average length of one second into three categories according to the age and/or gender properties of speakers. In the study where SVM approach based on GMM supervectors is used, speech signals are represented by an 39-element vector consisting of MFCC coefficients. In the tests where aGender database is used, the effect of GMM component number on the success is also investigated and the most suitable component number is determined. At the end of these tests, for the three-class gender category 87.28%, four-class age category 50.12% and seven-class age&gender category 48.05% success rates are achieved.

Keywords

signal representation; speaker recognition; support vector machines; 39-element vector; GMM supervector component number; MFCC coefficient; SVM approach; aGender database; age recognition; four-class age category; gender recognition; short-duration phone conversation classification; speaker recognition; speech signal representation; three-class gender category; Conferences; Gaussian mixture model; Mel frequency cepstral coefficient; Speech; Speech processing; Support vector machines; Age and gender recognition; GMM supervectors; Gaussian mixture model (GMM); Support Vector Machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2015 23th

Conference_Location

Malatya

Type

conf

DOI

10.1109/SIU.2015.7129936

Filename

7129936