Acoustic class specific VTLN-warping using regression class trees

In this paper, we study the use of different frequency warpfactors for different acoustic classes in a computationally efficient frame-work of Vocal Tract Length Normalization (VTLN). This is motivated by the fact that all acoustic classes do not exhibit similar spectral variations as a result of physiological differences in vocal tract, and therefore, the use of a single frequency-warp for the entire utterance may not be appropriate. We have recently proposed a VTLN method that implements VTLN-warping through a linear-transformation (LT) of the conventional MFCC features and efficiently estimates the warp-factor using the same sufficient statistics as that are used in CMLLR adaptation. In this paper we have shown that, in this framework of VTLN, and using the idea of regression class tree, we can obtain separate VTLN-warping for different acoustic classes. The use of regression class tree ensures that warp-factor is estimated for each class even when there is very little data available for that class. The acoustic classes, in general, can be any collection of the Gaussian components in the acoustic model. We have built acoustic classes by using data-driven approach and by using phonetic knowledge. Using WSJ database we have shown the recognition performance of the proposed acoustic class specific warp-factor both for the data driven and the phonetic knowledge based regression class tree definitions and compare it with the case of the single warp-factor. Index Terms: VTLN, Acoustic-Class Specific Warping, Regression Class Tree, Linear Transform