Paper Info
Title
Investigation of the effect of data duration and speaker gender on text-independent speaker recognition
Abstract
Duration of training/test data has a considerable effect on the performance of a speaker recognition system. In this paper, we analyze the effect of training and test data duration and speaker gender on the performance of speaker recognition systems. Gaussian mixture models-universal background model (GMM-UBM), vector quantization-universal background model (VQ-UBM), support vector machines-generalized linear discriminant sequence kernel (SVM-GLDS) and support vector machines with GMM supervectors (GSV-SVM) are the classifiers we use for speaker recognition. Experimental results conducted on NIST 2002 and NIST 2005 speaker recognition evaluation (SRE) databases show that recognition performance breaks down when short utterances are used for training and testing independent from the recognizer (e.g. equal error rate (EER) reduces from 10.33% to 27.86% on NIST 2005) and GSV-SVM system yields higher EER than other methods in the case of using short utterances. It is also shown that recognition accuracy for male speakers are higher than female independent from database and classifier.
Year
DOI
Venue
2013
10.1016/j.compeleceng.2012.09.014
Computers & Electrical Engineering
Keywords
Field
DocType
text-independent speaker recognition,support vector machine,support vector,male speaker,recognition accuracy,short utterance,speaker recognition,speaker gender,recognition performance,data duration,speaker recognition system,speaker recognition evaluation
Pattern recognition,Computer science,Support vector machine,Word error rate,Speech recognition,Speaker recognition,NIST,Test data,Artificial intelligence,Speaker diarisation,Linear discriminant analysis,Classifier (linguistics)
Journal
Volume
Issue
ISSN
39
2
0045-7906
Citations 
PageRank 
References 
5
0.45
26
Authors
2
Name
Order
Citations
PageRank
Cemal Hanilçi117111.23
Figen Ertaş2221.57