Importance of nasality measures for speaker recognition data selection and performance prediction
Howard Lei, Eduardo López-Gonzalo · 2009
We improve upon our measures relating feature vector distri-butions to speaker recognition (SR) performances for perfor-mance prediction and potential arbitrary data selection for SR, as described in [1]. In particular, we examine the means and variances of 11 features pertaining to nasality (each of which is denoted as a measure), computing them on feature vectors of phones to determine which measures give good SR performance prediction of phones. We’ve found that the combination of nasality measures give a 0.917 correlation with the Equal Error Rates (EERs) of phones on SRE08, exceeding the correlation of our previous best measure (mutual information) by 12.7%. When implemented in our data-selection scheme (which does not require a SR system to be run), the nasality measures allow us to select data with combined EER better than data selected via running a SR system in certain cases, at a fortieth of the computational costs. The nasality measures also require a tenth of the computational costs to compute compared to our previous best measure. Index Terms: Text-dependent speaker recognition, data selec-tion, nasality measures, relevance, redundancy