A Spearman correlation coefficient ranking for matching-score fusion on speaker recognition

This paper describes an attempt by making use of the Spearman rank correlation coefficient as a metric to measure features on the matching score fusion of speaker recognition. In the context of fusion technique of speaker recognition, the Spearman coefficient is introduced to measure the correlation between different acoustic features combining together such that the metric is able to find out an optimal solution for selecting a set of feasible features to achieve good performance. This coefficient can evaluate how far the relationship between the combined features in term of scores combined by two features. Throughout the evaluations for the scores combined by obtaining by different combination of acoustic features, we found that MFCC and residual phase are the optimal solution for feature selection. The outcomes indicate that the Spearman correlation coefficient is a reliable metric to measure features in the fusion of speaker recognition.