A speaker clustering algorithm is presented that is based on an eigenspace representation of Maximum Likelihood Linear Regression (MLLR) transformations and is used for training cluster-dependent regression-class trees for MLLR adaptation. It is shown that significant automatic speech recognition (ASR) system performance gains are possible by choosing the best regression-class tree structure for individual speakers. To take advantage of the potential gains, an algorithm for combining the MLLR mean transformations from cluster-specific trees is described that effectively results in a soft regression-class tree. In conversational speech recognition, only small overall improvements are obtained, but the number of speakers that have performance degradation due to adaptation is reduced by over 70%.
Cite as: Mandal, A., Ostendorf, M., Stolcke, A. (2006) Speaker clustered regression-class trees for MLLR adaptation. Proc. Interspeech 2006, paper 1763-Tue2BuP.11, doi: 10.21437/Interspeech.2006-346
@inproceedings{mandal06_interspeech,
author={Arindam Mandal and Mari Ostendorf and Andreas Stolcke},
title={{Speaker clustered regression-class trees for MLLR adaptation}},
year=2006,
booktitle={Proc. Interspeech 2006},
pages={paper 1763-Tue2BuP.11},
doi={10.21437/Interspeech.2006-346}
}