In this paper, we present a linear transformation (LT) to obtain warped features from unwarped features during vocal-tract length normalisation (VTLN). This LT between the warped and unwarped features is obtained within the conventional MFCC framework without any modification in the signal processing steps involved during the feature extraction stage. Further using the proposed LT, we study the effect of the Jacobian on the VTLN performance and show that it provides additional improvement in the recognition performance. The Jacobian of the proposed LT is simply the determinant of the LT matrix. Jacobian compensation is not done in conventional VTLN as the relation between warped and unwarped features is not known. We also study the effect of cepstral variance normalisation (CVN), which is often used as an approximation for Jacobian compensation in conventional VTLN. We show that the proposed Jacobian compensation gives better or comparable performance when compared to CVN. Copyright © 2008 ISCA.