Speaker adaptation based on confidence-weighted training

This paper presents a novel method to enhance the performance of traditional speaker adaptation algorithm using discriminative adaptation procedure based on a novel confidence measure and non-linear weighting. Regardless of the distribution of the adaptation data, traditional model adaptation methods incorporate the adaptation data undiscriminatingly. When the data size is small and the parameter tying is extensive, adaptation based on outliers can be detrimental. A way to discriminate the contribution of each data in the adaptation is to incorporate a confidence measure based on likelihood. We evaluate and compare the performances of the proposed weighted SMAP (WSMAP) which controls the contribution of each data by sigmoid weighting using a novel confidence measure. The effectiveness of the proposed algorithm is experimentally verified by adapting native speaker models to nonnative speaker environment using TIDIGIT.