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

Audio and Speech Processing;Computer Vision and Pattern Recognition;Machine Learning