A linguistically-motivated speaker recognition front-end through session variability compensated cepstral trajectories in phone units

In this paper a new linguistically-motivated front-end is presented showing major performance improvements from the use of session variability compensated cepstral trajectories in phone units. Extending our recent work on temporal contours in linguistic units (TCLU), we have combined the potential of those unit-dependent trajectories with the ability of feature domain factor analysis techniques to compensate session variability effects, which has resulted in consistent and discriminant phone-dependent trajectories across different recording sessions. Evaluating with NIST SRE04 English-only 1s1s task, we report EERs as low as 5.40% from the trajectories in a single phone, with 29 different phones producing each of them EERs smaller than 10%, and additionally showing an excellent calibration performance per unit. The combination of different units shows significant complementarity reporting EERs as 1.63% (100×DCF=0.732) from a simple sum fusion of 23 best phones, or 0.68% (100×DCF=0.304) when fusing them through logistic regression.