The National Institute of Standards and Technology (NIST) Speaker Recognition Evaluations (SRE) are an ongoing series of projects conducted by NIST. In the NIST SRE, speaker detection performance is measured using a detection cost function, which is defined as a weighted sum of probabilities of type I error and type II error. The sampling variability can result in measurement uncertainties of the detection cost function. Hence, while evaluating and comparing the performances of speaker recognition systems, the uncertainties of measures must be taken into account. In this article, the uncertainties of detection cost functions in terms of standard errors (SE) and confidence intervals are computed using the nonparametric two-sample bootstrap methods based on our extensive bootstrap variability studies on large datasets conducted before. The data independence is assumed because the bootstrap results of SEs matched very well with the analytical results of SEs using the Mann-Whitney statistic for independent and identically distributed samples if the metric of area under a receiver operating characteristic curve is employed. Examples are provided.
Proceedings Title: SPIE Defense Security Sensing
Conference Dates: April 25-29, 2011
Conference Location: Orlando, FL
Pub Type: Conferences
Speaker recognition evaluation, Biometrics, Bootstrap, Uncertainty, Standard error, Confidence interval.