Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Strategies for Likelihood Ratio Computation in Forensic Voice
  Comparison with Automatic Systems   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.

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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
18
The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-ex
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The categories of this paper are: math