The offender recording (a speaker recorded at the scene of a crime) is verified by determining the similarity of the offender recording's typicality to a recording of a suspect.
A universal background model (UBM) is generated via the training of a parametric Gaussian Mixture Model (GMM) that reflects the distribution of feature vectors in a reference population. Every comparison recording is used to derive a GMM from the UBM by adaptation of the model parameters. Similarity is measured through computation of the likelihoods of the offender recordings in the GMM while typicality is measured by computation of the likelihoods of the offender recordings in the UBM. The verification is expressed as the likelihood ratio of these likelihood values.
While fully unsupervised automatic verification is performed with a binary decision using a likelihood ratio threshold and is used in biometric commercial applications, the usage of the likelihood ratio as an expression of the strength of the evidence in forensic speaker verification has become an important issue.