MAP and sub-word level T-norm for text-dependent speaker recognition

Proceedings of Interspeech 2008, Brisbane (Australia)This paper presents improvements in text-dependent speaker recognition based on the use of Maximum A Posteriori (MAP) adaptation of Hidden Markov Models and the use of new sub-word level T-Normalization procedures. Results on the YOHO corpus show that the use of MAP adaptation provides a relative improvement of 22.6% in Equal Error Rate (EER) in comparison with Baum-Welch retraining and Maximum Likelihood Linear Regression (MLLR) adaptation. The newly proposed sub-word level T-Normalization procedures provide additional relative improvements, particularly for small cohorts, of up to 20% in EER in comparison with the normal utterance-level T-Normalization.This work was funded by the Spanish Ministry of Science and Technology under project TEC2006-13170-C02-01