Abstract
We developed measures relating feature vector distributions to speaker recognition (SR) performances for performance prediction and potential arbitrary data selection for SR. We examined the measures of mutual information, kurtosis, correlation, and measures pertaining to intra- and inter-speaker variability. We applied the measures on feature vectors of phones to determine which measures gave good SR performance prediction of phones standalone and in combination. We found that mutual information had an -83.5% correlation with the Equal Error Rates (EERs) of each phone. Also, Pearson's correlation between the feature vectors of two phones had a -48.6% correlation with the relative EER improvement of the score-level combination of the phones. When implemented in our new data-selection scheme (which does not require a SR system to be run), the measures allowed us to select data with 2.13% overall EER improvement (on SRE08) over data selected via a brute-force approach, at a fifth of the computational costs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.