## Enhancements to Transformation-Based Speaker Adaptation: Principal Component and Inter-Class Maximum Likelihood Linear Regression (2000)

Citations: | 5 - 1 self |

### BibTeX

@TECHREPORT{Doh00enhancementsto,

author = {Sam-joo Doh},

title = {Enhancements to Transformation-Based Speaker Adaptation: Principal Component and Inter-Class Maximum Likelihood Linear Regression},

institution = {},

year = {2000}

}

### OpenURL

### Abstract

iii Abstract In this thesis we improve speech recognition accuracy by obtaining better estimation of linear transformation functions with a small amount of adaptation data in speaker adaptation. The major contributions of this thesis are the developments of two new adaptation algorithms to improve maximum likelihood linear regression. The first one is called principal component MLLR (PC-MLLR), and it reduces the variance of the estimate of the MLLR matrix using principal component analysis. The second one is called inter-class MLLR, and it utilizes relationships among different transformation functions to achieve more reliable estimates of MLLR parameters across multiple classes. The main idea of PC-MLLR is that if we estimate the MLLR matrix in the eigendomain, the variances of the components of the estimates are inversely proportional to their eigenvalues. Therefore we can select more reliable components to reduce the variances of the resulting estimates and to improve speech recognition accuracy. PC-MLLR eliminates highly variable components and chooses the principal components corresponding to the largest eigenvalues. If all the component are used, PC-MLLR becomes the same as conventional MLLR. Choosing fewer principal components increases the bias of the estimates which can reduce recognition accuracy. To compensate for this problem, we developed weighted principal component MLLR (WPC-MLLR). Instead of eliminating some of the components, all the components in WPC-MLLR are used after applying weights that minimize the mean square error. The component corresponding to a larger eigenvalue has a larger weight than the component corresponding to a smaller eigenvalue. As more adaptation data become available, the benefits from these methods may become smaller because ...