A computationally efficient approach for acoustic class specific VTLN using regression tree

Abstract

In this paper a novel frame-work for acoustic class specific vocal tract length normalization (VTLN) is developed. Unlike the computationally expensive grid search involved in conventional VTLN, the proposed technique works in the joint paradigm of linear transform VTLN and the txpectation maximization algorithm, and uses Regression class tree for robustness. Experimental results are demonstrated on two wall street journal (WSJ) test sets Nov92 eval and Dev-93 with the acoustic model being trained on the WSJ-284 set. It is found that the proposed acoustic class specific VTLN provides consistent improvements in word accuracies in comparison to the conventional VTLN which uses single warp-factor for spectral warping.