Current models of speech motor control: A control-theoretic overview of architectures and properties

Abstract

This paper reviews the current state of several formal models of speech motor control, with particular focus on the low-level control of the speech articulators. Further development of speech motor control models may be aided by a comparison of model attributes. The review builds an understanding of existing models from first principles, before moving into a discussion of several models, showing how each is constructed out of the same basic domain-general ideas and components—e.g., generalized feedforward, feedback, and model predictive components. This approach allows for direct comparisons to be made in terms of where the models differ, and their points of agreement. Substantial differences among models can be observed in their use of feedforward control, process of estimating system state, and method of incorporating feedback signals into control. However, many commonalities exist among the models in terms of their reliance on higher-level motor planning, use of feedback signals, lack of time-variant adaptation, and focus on kinematic aspects of control and biomechanics. Ongoing research bridging hybrid feedforward/feedback pathways with forward dynamic control, as well as feedback/internal model-based state estimation, is discussed.

Singing training predicts increased insula connectivity with speech and respiratory sensorimotor areas at rest

“…Together, they revealed a hierarchically organized structure-function relationship in which basic vocal pattern are generated in the brain stem and the periaqueductal gray, which interact with limbic regions, whereas volitional sensorimotor control of complex vocalizations takes place in the cortex (Hage and Nieder, 2016;Simonyan and Horwitz, 2011). The presumed interactions between these regions are described and formalized by neurocomputational models in the context of speech motor processes (Bouchard et al, 2013;Guenther, 2016;Guenther and Hickok, 2015;Hickok, 2017;Houde and Chang, 2015;Parrell et al, 2017;Rodriguez-Fornells et al, 2009;Tian and Poeppel, 2010). Specifically, the DIVA (Directions Into Velocities of Articulators) model suggests that dorsal stream speech-motor control involve a sensory integration interface encompassing auditory, ventral somatosensory, and inferior parietal cortices, which interact via the arcuate fasciculus with the articulatory motor network located in ventral primary-and premotor regions (Guenther and Hickok, 2015).…”

A cross-language study on feedforward and feedback control of voice intensity in Chinese–English bilinguals

“…This mechanism emphasizes its independence from the sensory feedback associated with articulation (Guenther, 2016). Therefore, feedforward control enables the rapidity of speech, but lacks the ability to monitor errors in speech output (Parrell et al, 2019). Because we live in time-varying and unpredictable surroundings, feedforward control alone cannot ensure effective speech.…”

“…The feedback control system is indispensable in speech motor control, allowing speakers to regulate movements and interact well with the environment in presence of external perturbations (Bays & Wolpert, 2006). Parrell et al (2019) proposed a special case of feedforward control by eliminating auditory feedback involvement. An auditory prediction (realized using an internal forward model) is the possible outcome of articulatory movement before auditory feedback is received.…”

Establishing metrics and control laws for the learning process: ball and beam balancing

“…The underlying neural mechanism involves many levels of sensory and motor integration. This complexity makes it difficult to uncover the guiding principles which underlie dexterity (for a recent review of the control of complex motor tasks see Parrell et al (2019)). However, indirect evidence that such principles may exist is provided by the observation that overall cortical activation decreases as dexterity improves with a selective enhancement of these cortical regions most relevant for task performance (Bilalic 2017;Hatfield and Hillman 2001;Milton et al 2007;Puttemans et al 2005).…”