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Enhancing Vocal Tract Length Normalization with Elastic Registration for Automatic Speech Recognition

Vocal tract length normalization (VTLN) is commonly applied utterance-wise with a warping function that makes the assumption of a linear dependence between the vocal tract length and the location of the formants. In this work we propose a datadriven method for enhancing the performance of systems that already use standard VTLN. The method is based on elastic registration to estimate optimal non-parametric transformations to further reduce inter-speaker variabilities. Results show that the proposed method can increase the performance of monophone systems such that it reaches that of a triphone system.