TADA: An enhanced, portable Task Dynamics model in M A T L A B

Abstract

A portable computational system called TADA was developed for the Task Dynamic model of speech motor control [Saltzman and Munhall, Ecol. Psychol. 1, 333–382 (1989)]. The model maps from a set of linguistic gestures, specified as activation functions with corresponding constriction goal parameters, to time functions for a set of model articulators. The original Task Dynamic code was ported to the (relatively) platform-independent MATLAB environment and includes a MATLAB version of the Haskins articulatory synthesizer, so that articulator motions computed by the Task Dynamic model can be used to generate sound. Gestural scores can now be edited graphically and the effects of gestural score changes on the models output evaluated. Other new features of the system include: (1) A graphical user interface that displays the input gestural scores, output time functions of constriction goal variables and articulators, and an animation of the resulting vocal-tract motion; (2) Integration of the Task Dynamic model with the prosodic clock-slowing, pi-gesture model of Byrd and Saltzman [J. Phonetics 31, 149–180 (2003)]. This now allows prosody-driven slowing to be applied to the full set of active gestures and its effects to be evaluated perceptually. [Work supported by NIH.]

The coarticulation/invariance scale: Mutual information as a measure of coarticulation resistance, motor synergy, and articulatory invariance

“…So, for example, the jaw has a higher weight than the lower lip for the achievement of the lip aperture task. So far these weights have been gleaned from subjective evaluation of the results of speech production experiments and through back-fitting of the weight parameters based on speech synthesis using the task dynamic simulator TADA (Task Dynamic Toolkit) (Nam et al, 2004). Another measure of synergy has recently been explored in simulating results from babbling, namely, that the distance between the target positions of different segments can be converted directly into a synergy measure (Nam et al, 2013).…”

Speech Prosody 2016

“…The current version of CPP has included four models: 1) the Command-Response (CR) model [1], 2) the Autosegmental-Metrical (AM) theory [7][8], 3) the Task Dynamic (TD) model [3], and 4) the Parallel Encoding and Target Approximation (PENTA) model [2]. In terms of learning algorithms, at the present stage, all four models have been implemented with local curve fitting capabilities, while only PENTA has been implemented with full-fledged global optimization algorithms in PENTAtrainer2 [9].…”

Learning vocal tract variables with multi-task kernels

“…Acoustic-articulatory database is generated by the TAsk Dynamics Application (TADA [8]) model, which is a computational implementation of articulatory phonology. The generated dataset consists of acoustic signals for 416 words chosen from the Wisconsin X-ray microbeam data [21] and corresponding Vocal Tract (VT) trajectories sampled at 5 msec.…”

“…In a recent work, Mitra et al [2] suggest the use of the TAsk Dynamics Application (TADA [8]) model to generate acoustic-articulatory database which contains synthetic speech and the corresponding Vocal Tract (VT) time functions. Their results show that tract variables can be better candidates than EMA trajectories for articulatory feature based ASR systems.…”