We improve upon measures relating feature vector distributions to speaker recognition (SR) performances for SR performance prediction and arbitrary data selection. In particular, we examine the means and variances of 11 features pertaining to nasality (resulting in 22 measures), 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 require a tenth of the computational costs compared to our previous best measure.
Bibliographic reference. Lei, Howard / Lopez-Gonzalo, Eduardo (2009): "Importance of nasality measures for speaker recognition data selection and performance prediction", In INTERSPEECH-2009, 888-891.