Speaker adaptation in continuous speech recognition via estimation of correlated mean vectors

Recent attempts to improve the recognition performance of a semi-continuous version of the CMU SPHINX system (SPHINX-SC) through the use of speaker adaptation are described. The authors´ approach to speaker adaptation is to use multivariate parameter estimation procedures to update the mean values of the component densities which comprise the system´s codebook, given the speaker-specific observations. The authors have developed a least mean square (LMS) algorithm which produces a faster rate of convergence than the Bayesian estimator, at the expense of a finite misadjustment. This estimate is similar in form to an LMS transversal filter, and is computationally more efficient than the Bayesian estimate. Results show an overall reduction of 2.0 to 3.4% in word error rate due to adaptation for a set of 11 speakers from the DARPA resource management task