Trial-Based Calibration for Speaker Recognition in Unseen Conditions

Abstract

This work presents Trial-Based Calibration (TBC), a novel, automated calibration technique robust to both unseen and widely varying conditions. Motivated by the approach taken by forensic experts in speaker recognition, TBC delays estimating calibration parameters until trial-time when acoustic and behavioral conditions of both sides of the trial are known. An audio characterization system is used to select a small subset of candidate calibration audio samples that best match the conditions of the enrollment sample and a subset that resembles the test conditions.  Calibration parameters learned from the target and impostor trials are generated by pairing up these samples and then used to calibrate the score output from the SID system.  Evaluated on a diverse, pooled collection of 11 different databases which 14 distinct conditions, the proposed TBC outperforms traditional calibration methods and obtains calibration performance similar to having an ideally matched calibration set.