Literature DB >> 31067944

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

Benjamin Parrell1, Adam C Lammert2, Gregory Ciccarelli2, Thomas F Quatieri2.   

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.

Year:  2019        PMID: 31067944     DOI: 10.1121/1.5092807

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  9 in total

Review 1.  Modeling the Role of Sensory Feedback in Speech Motor Control and Learning.

Authors:  Benjamin Parrell; John Houde
Journal:  J Speech Lang Hear Res       Date:  2019-08-29       Impact factor: 2.297

2.  LaDIVA: A neurocomputational model providing laryngeal motor control for speech acquisition and production.

Authors:  Hasini R Weerathunge; Gabriel A Alzamendi; Gabriel J Cler; Frank H Guenther; Cara E Stepp; Matías Zañartu
Journal:  PLoS Comput Biol       Date:  2022-06-23       Impact factor: 4.779

3.  Intact Correction for Self-Produced Vowel Formant Variability in Individuals With Cerebellar Ataxia Regardless of Auditory Feedback Availability.

Authors:  Benjamin Parrell; Richard B Ivry; Srikantan S Nagarajan; John F Houde
Journal:  J Speech Lang Hear Res       Date:  2021-04-26       Impact factor: 2.297

4.  Motoric Mechanisms for the Emergence of Non-local Phonological Patterns.

Authors:  Sam Tilsen
Journal:  Front Psychol       Date:  2019-09-26

5.  Learning Speech Production and Perception through Sensorimotor Interactions.

Authors:  Shihab Shamma; Prachi Patel; Shoutik Mukherjee; Guilhem Marion; Bahar Khalighinejad; Cong Han; Jose Herrero; Stephan Bickel; Ashesh Mehta; Nima Mesgarani
Journal:  Cereb Cortex Commun       Date:  2020-11-27

6.  Sigma-Lognormal Modeling of Speech.

Authors:  C Carmona-Duarte; M A Ferrer; R Plamondon; A Gómez-Rodellar; P Gómez-Vilda
Journal:  Cognit Comput       Date:  2021-02-07       Impact factor: 5.418

7.  Perturbing the consistency of auditory feedback in speech.

Authors:  Daniel R Nault; Takashi Mitsuya; David W Purcell; Kevin G Munhall
Journal:  Front Hum Neurosci       Date:  2022-08-25       Impact factor: 3.473

8.  Examining the Relationship Between Speech Perception, Production Distinctness, and Production Variability.

Authors:  Hung-Shao Cheng; Caroline A Niziolek; Adam Buchwald; Tara McAllister
Journal:  Front Hum Neurosci       Date:  2021-05-28       Impact factor: 3.169

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

Authors:  Gergely Buza; John Milton; Laszlo Bencsik; Tamas Insperger
Journal:  Biol Cybern       Date:  2020-01-18       Impact factor: 2.086

  9 in total

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