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This paper explores several strategies for Forensic Voice Comparison (FVC),aimed at improving the performance of the LRs when using generative Gaussianscore-to-LR models. First, different anchoring strategies are proposed, withthe objective of adapting the LR computation process to the case at hand,always respecting the propositions defined for the particular case. Second, afully-Bayesian Gaussian model is used to tackle the sparsity in the trainingscores that is often present when the proposed anchoring strategies are used.Experiments are performed using the 2014 i-Vector challenge set-up, whichpresents high variability in a telephone speech context. The results show thatthe proposed fully-Bayesian model clearly outperforms a more commonMaximum-Likelihood approach, leading to high robustness when the scores totrain the model become sparse.

Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice Comparison with Automatic Systems