|Mitchell Mclaren, Aaron Lawson, Luciana Ferrer, Nicolas Scheffer and Yun Lei|
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 generated by pairing up these samples are then used to calibrate the score output from the speaker identification system. Evaluated on a diverse, pooled collection of 5 different databases with 14 distinct conditions, the proposed TBC outperforms traditional calibration methods and obtains calibration performance similar to having an ideally matched calibration set.