Please use this identifier to cite or link to this item:
Title: Age and Gender Recognition of a Speaker from Short-duration Phone Conversations
Authors: Yucesoy, Ergun
Nabiyev, Vasif V.
Ordu Üniversitesi
Keywords: Age and gender recognition, Gaussian mixture model (GMM), GMM supervectors, Support Vector Machine
Issue Date: 2015
Publisher: IEEE-NEW YORK
Citation: Yücesoy, E., Nabiyev, VV. (2015). Age and Gender Recognition of a Speaker from Short-duration Phone Conversations. , 751-754
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.
Description: WoS Categories: Engineering, Electrical & Electronic; Telecommunications
Web of Science Index: Conference Proceedings Citation Index - Science (CPCI-S)
Research Areas: Engineering; Telecommunications
Conference Title: 23nd Signal Processing and Communications Applications Conference (SIU)
ISBN: 978-1-4673-7386-9
ISSN: 2165-0608
Appears in Collections:Makale Koleksiyonu

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.