Voice biometrics using linear Gaussian model

This study introduces a linear Gaussian model-based framework for voice biometrics. The model works with discrete-time linear dynamical systems. The study motivation is to use the linear Gaussian modelling method in voice biometrics, and show that the accuracy offered by the linear Gaussian modelling method is comparable with other state-of-the-art methods such as Probabilistic Linear Discriminant Analysis and two-covariance model. An expectation-maximisation algorithm is derived to train the model and a Bayesian solution is used to calculate the log-likelihood ratio score of all trials of speakers. This approach performed well on the core-extended conditions of the NIST 2010 Speaker Recognition Evaluation, and is competitive compared with the Gaussian probabilistic linear discriminant analysis, in terms of normalised decision cost function.