This paper describes the application of a full Bayesian significance test (FBST) to compute evidence intervals in forensic speaker comparison (FSC). In the FBST approach, the challenge is to apply the test to a large number of observations and to formulate an equation to solve the test quickly. The contribution of the present work is that it proposes an application of the FBST to FSC and develops a method to calculate the FBST for the distribution of expected values (mean) with unknown variance without using Monte Carlo Markov chains (MCMC). Comparisons with other interval inference methodologies indicate that the evidence interval size is 49% greater than that computed with the Gosset approach. The evidence interval presented 71% fewer classification errors than the punctual inference did for the signal-to-noise ratio (SNR) of 17?dB.
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