Accurate Log-Likelihood Ratio Estimation by using Test Statistical Model for Speaker Verification

Author

Matrouf, Driss ; Bonastre, Jean-François

Author_Institution

LIA, Univ. d´´Avignon, Avignon

fYear

2006

fDate

28-30 June 2006

Firstpage

1

Lastpage

5

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

Keywords

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; Adaptation model; Computational efficiency; Equations; Error analysis; NIST; Performance gain; Protocols; Speaker recognition; Speech; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Speaker and Language Recognition Workshop, 2006. IEEE Odyssey 2006: The

Conference_Location

San Juan

Print_ISBN

1-424400471-1

Electronic_ISBN

1-4244-0472-X

Type

conf

DOI

10.1109/ODYSSEY.2006.248139

Filename

4013556