Statistical Analysis for Speaker Recognition Evaluation With Data Dependence and Three Score Distributions

The speaker recognition evaluation is conducted in a framework in which three score distributions and two decision thresholds are employed, and the statistic of interest is an average of the two weighted sums of the probabilities of type I and type II errors at the two thresholds correspondingly. And data dependence caused by multiple use of the same subjects exists ubiquitously in order to generate more samples because of limited resources. Under such circumstances, statistical analysis is carried out. First, the standard error (SE) of measure is estimated using the nonparametric three-sample two-layer bootstrap algorithm on a two-layer data structure constructed after dataset optimization due to data dependence, based upon our prior rigorous statistical research in ROC analysis on large datasets with data dependence. Second, only based on such SEs, can the one-classifier and two-classifier significance testing in statistics be carried out to provide quantitative information in terms of the significance level, i.e., p-value, while dealing with evaluation and comparison of classifiers. In comparison, the positive correlation coefficient must be taken into account, which is computed using a synchronized resampling algorithm; otherwise, the likelihood of detecting the statistical significance of difference between the performance levels of two classifiers can be wrongly reduced.