5th International Conference on Spoken Language Processing

Sydney, Australia
November 30 - December 4, 1998

Linear Discriminant - A New Criterion For Speaker Normalization

Martin Westphal (1), Tanja Schultz (1), Alex Waibel (2)

(1) Interactive Systems Labs, Germany
(2) Interactive Systems Labs, USA

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 interclass 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 recognition tasks.

Full Paper

Bibliographic reference.  Westphal, Martin / Schultz, Tanja / Waibel, Alex (1998): "Linear discriminant - a new criterion for speaker normalization", In ICSLP-1998, paper 0755.