A model of gestural sequencing in speech is proposed that aspires to producing biologically plausible fluent and efficient movement in generating an utterance. We have previously proposed a modification of the well-known task dynamic implementation of articulatory phonology such that any given articulatory movement can be associated with a quantification of effort (Simko & Cummins, 2010). To this we add a quantitative cost that decreases as speech gestures become more precise, and hence intelligible, and a third cost component that places (...) a premium on the duration of an utterance. Together, these three cost elements allow us to derive algorithmically optimal sequences of gestures and dynamical parameters for generating articulator movement. We show that the optimized movement displays many timing characteristics that are representative of real speech movement, capturing subtle details of relative timing between gestures. Optimal movement sequences also display invariances in timing that suggest syllable-level coordination for CV sequences. We explore the behavior of the model as prosodic context is manipulated in two dimensions: clarity of articulation and speech rate. Smooth, fluid, and efficient movements result. (shrink)

Current models of speech motor control rely on either trajectory-based control (DIVA, GEPPETO, ACT) or a dynamical systems approach based on feedback control (Task Dynamics, FACTS). While both approaches have provided insights into the speech motor system, it is difficult to connect these findings across models given the distinct theoretical and computational bases of the two approaches. We propose a new extension of the most widely used dynamical systems approach, Task Dynamics, that incorporates many of the strengths of trajectory-based approaches, (...) providing a way to bridge the theoretical divide between what have been two separate approaches to understanding speech motor control. The Task Dynamics (TD) model posits that speech gestures are governed by point attractor dynamics consistent with a critically damped harmonic oscillator. Kinematic trajectories associated with such gestures should therefore be consistent with a second-order dynamical system, possibly modified by blending with temporally overlapping gestures or altering oscillator parameters. This account of observed kinematics is powerful and theoretically appealing, but may be insufficient to account for deviations from predicted kinematics—i.e., changes produced in response to some external perturbations to the jaw, changes in control during acquisition and development, or effects of word/syllable frequency. Optimization, such as would be needed to minimize articulatory effort, is also incompatible with the current TD model, though the idea that the speech production systems economizes effort has a long history and, importantly, also plays a critical role in current theories of domain-general human motor control. To address these issues, we use Dynamic Movement Primitives (DMPs) to expand a dynamical systems framework for speech motor control to allow modification of kinematic trajectories by incorporating a simple, learnable forcing term into existing point attractor dynamics. We show that integration of DMPs with task-based point-attractor dynamics enhances the potential explanatory power of TD in a number of critical ways, including the ability to account for external forces in planning and optimizing both kinematic and dynamic movement costs. At the same time, this approach preserves the successes of Task Dynamics in handling multi-gesture planning and coordination. (shrink)

The proposed model consists of 1) a Phonological Planning Component to plan the symbolic and relational goals for an utterance, 2) a Phonetic Planning Component to plan the quantitative details of the acoustic goals and how they will be achieved articulatorily, and 3) a Motor-Sensory Implementation Component to ensure that the goals are achieved on time. The temporal characteristics specified in the Phonetic Planning Component include durations between acoustic landmarks, as well as parameters of Lee’s TauG-Guidance equation, which determine how (...) a movement evolves over time. Movement coordination is assumed to be based on movement endpoints (rather than onsets) and accomplished via Tau-coupling (Lee 1998). Both the Phonetic Planning and Motor-Sensory Implementation components are assumed to make use of general-purpose, phonology-extrinsic, timekeeping mechanisms to represent, specify, and track time. Supporting evidence includes greater timing precision at movement endpoints compared to other parts of movements, suggesting the separate control of the timing of movement endpoints compared to other parts of movement. This evidence challenges models in which all parts of a movement trajectory are controlled by the same equation of motion, but supports models in which a) each abstract, symbolic phonological representation maps onto spatial and temporal characteristics of the part(s) of movement most closely related to the goal of producing a planned set of acoustic cues to signal the phonological contrast (often the endpoint), b) movements are coordinated primarily based on the goal-related part of movement, and c) speakers give priority to the accurate implementation of the part(s) of movement most closely related to the phonological goals. Further support for phonology-extrinsic timing comes from three types of evidence suggesting that surface durations are represented during speech production. Finally, phonology-extrinsic timing is supported by greater timing variability for repetitions of longer intervals, assumed to be due to noise in a general-purpose (and phonology-extrinsic) timekeeping process. The evidence appears to be incompatible with models that have a unified Phonology/Phonetics Component, with models which do not represent the surface timing of phonetic events, nor with models in which timing in speech production is not represented, specified and tracked by a general-purpose timekeeping mechanisms. (shrink)