Author/Authors
HANİLÇİ, Cemal Uludağ University - Faculty of Engineering and Architecture - Dept of Electronic Engineering, Turkey , ERTAŞ, Figen Uludağ University - Faculty of Engineering and Architecture - Dept of Electronic Engineering, Turkey
Title Of Article
EFFECTS OF BACKGROUND DATA DURATION ON SPEAKER VERIFICATION PERFORMANCE
شماره ركورد
34743
Abstract
Gaussian mixture models with universal background model (GMM-UBM) and vector quantization with universal background model (VQ-UBM) are the two well-known classifiers used for speaker verification. Generally, UBM is trained with many hours of speech from a large pool of different speakers. In this study, we analyze the effect of data duration used to train UBM on text-independent speaker verification performance using GMM-UBM and VQ-UBM modeling techniques. Experiments carried out NIST 2002 speaker recognition evaluation (SRE) corpus show that background data duration to train UBM has small impact on recognition performance for GMM-UBM and VQ-UBM classifiers.
From Page
111
NaturalLanguageKeyword
Speaker verification , Gaussian mixture model , Vector Quantization , Universal background model
JournalTitle
Uludağ University Journal of The Faculty of Engineering
To Page
119
JournalTitle
Uludağ University Journal of The Faculty of Engineering
Link To Document