Paper Info
Title
Accurate Log-Likelihood Ratio Estimation by using Test Statistical Model for Speaker Verification
Abstract
In this paper we propose an accurate estimation of the log-likelihood ratio (LLR) thanks to a statistical modelling of the test data. This work takes place within the framework of GMM/UBM based speaker verification. Modelling the test data using a statistical model like a GMM shows several advantages, and particularly it allows to reduce the influence of out-of-domain data thanks to the underlined statistical model. In this paper, we explore the interests of such methods, using a GMM modelling of the test data. We propose also an extension of this approach to the MAP-based speaker model adaptation. Some experiments based on the NIST SRE 2005 protocol are presented and show a significant gain (between 4% and 5% in relative compared to our NIST GMM/UBM baseline) by using our LLR estimation
Year
DOI
Venue
2006
10.1109/ODYSSEY.2006.248139
Odyssey
Keywords
Field
DocType
gaussian processes,maximum likelihood estimation,protocols,speaker recognition,statistical testing,gmm-ubm,gaussian mixture model,llr estimation,map-based speaker model adaptation,nist sre 2005 protocol,speaker recognition evaluation,log-likelihood ratio,speaker verification,test statistical model,universal background model,nist,log likelihood ratio,testing,speech
Likelihood-ratio test,Pattern recognition,Computer science,Speech recognition,NIST,Speaker recognition,Test data,Artificial intelligence,Gaussian process,Statistical model,Statistical hypothesis testing,Mixture model
Conference
ISBN
Citations 
PageRank 
1-4244-0472-X
2
0.41
References 
Authors
6
2
Name
Order
Citations
PageRank
Driss Matrouf140441.80
Jean-François Bonastre26410.60