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.
Bibliographic reference. Toledano, Doroteo T. / Hernandez-Lopez, Daniel / Esteve-Elizalde, Cristina / Gonzalez-Rodriguez, Joaquin / Pozo, Ruben Fernandez / Gomez, Luis Hernandez (2008): "MAP and sub-word level t-norm for text-dependent speaker recognition", In INTERSPEECH-2008, 1933-1936.