Linear discriminant - a new criterion for speaker normalization
Martin Westphal, Tanja Schultz, Alex Waibel · 1998
In Vocal Tract Length Normalization (VTLN) a linear or nonlinear frequency transformation compensates for different vocal tract lengths. Finding good estimates for the speaker specific warp parameters is a critical issue. Despite good results using the Maximum Likelihood criterion to find parameters for a linear warping, there are concerns using this method. We searched for a new criterion that enhances the inter-class separability in addition to optimizing the distribution of each phonetic class. Using such a criterion, Linear Discriminant Analysis determines a linear transformation in a lower dimensional space. For VTLN, we keep the dimension constant and warp the training samples of each speaker such that the Linear Discriminant is optimized. Although that criterion depends on all training samples of all speakers it can iteratively provide speaker specific warp factors. We discuss how this approach can be applied in speech recognition and present first results on two different recog...