Leveraging Speaker-dependent Variation of Adaptation

Abstract

This work introduces an automatic procedure for determining the size of regression class trees for individual speakers using an ensemble of speaker-level features to control the number of transformations, if any, that should be estimated by maximum likelihood linear regression. Experiments with a state-of-the-art speech recognition system that uses this procedure show improvements in word error rate for conversational telephone speech.