Aggregate a Posteriori Linear Regression for Speaker Adaptation
Chih‐Hsien Huang, Jen‐Tzung Chien · 2006
In this paper, we present a rapid and discriminative speaker adaptation algorithm for speech recognition. The adaptation paradigm is constructed under the popular linear regression transformation framework. Attractively, we estimate the regression matrices from the speaker-specific adaptation data according to the aggregate a posteriori criterion, which can be expressed in a form of classification error function. The goal of the proposed aggregate a posteriori linear regression (AAPLR) is to estimate the discriminative linear regression matrices for transformation-based adaptation so that the classification errors can be minimized. Different from minimum classification error linear regression (MCELR), the AAPLR algorithm has a closed-form solution to achieve rapid speaker adaptation. The experimental results reveal that AAPLR speaker adaptation does improve speech recognition performance with moderate computational cost compared to the maximum likelihood linear regression (MLLR), maximum a posteriori linear regression (MAPLR) and MCELR.