Speech can be represented as a constellation of constricting events, gestures, which are defined at vocal tract variables, in a form of gestural score. Gestures and their output trajectories, tract variables, which are available only in synthetic speech, have recently been shown to improve the ASR performance. We introduce a procedure to annotate gestures on natural speech database, a landmark-based time warping method. For a given speech, Haskins Laboratories TADA model is used to generate a gestural score and acoustic output, and an optimal gestural score is estimated through iterative time-warping processes based on landmark (phone) comparison.