2001: A Speaker Odyssey -
The Speaker Recognition Workshop
June 18-22, 2001
Forensic Speaker Recognition Based on a Bayesian Framework and
Gaussian Mixture Modelling (GMM)
Didier Meuwly, Andrzej Drygajlo
The goal of this paper is to establish scientifically founded methodology for forensic automatic
speaker recognition. The interpretation of recorded speech as evidence in the forensic context
presents particular challenges. The means proposed in the paper for dealing with them is through
Bayesian inference. This leads to the formulation of a likelihood ratio measure of evidence which
weighs the evidence in favour of two competing hypotheses: 1) the suspected speaker (source) is
the speaker of the questioned recording (trace), 2) the speaker at the origin of the questioned
recording is not the suspected speaker. The state-of-the-art automatic recognition system using
Gaussian mixture model (GMM) is adapted to the Bayesian interpretation (BI) framework with the
models of the within-source variability of the suspected speaker and the between-source
variability of the questioned recording. This double-statistical approach (BI-GMM) gives an
adequate solution for the interpretation of the recorded speech as evidence in the judicial process.
Examples provided are for telephone quality speech recordings which account for a very large
proportion of all forensic material for speaker recognition.
Meuwly, Didier / Drygajlo, Andrzej (2001):
"Forensic speaker recognition based on a Bayesian framework and
Gaussian mixture modelling (GMM)",
In ODYSSEY-2001, 145-150.