Literature DB >> 31465712

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

Benjamin Parrell1, John Houde2.   

Abstract

Purpose While the speech motor system is sensitive to feedback perturbations, sensory feedback does not seem to be critical to speech motor production. How the speech motor system is able to be so flexible in its use of sensory feedback remains an open question. Method We draw on evidence from a variety of disciplines to summarize current understanding of the sensory systems' role in speech motor control, including both online control and motor learning. We focus particularly on computational models of speech motor control that incorporate sensory feedback, as these models provide clear encapsulations of different theories of sensory systems' function in speech production. These computational models include the well-established directions into velocities of articulators model and computational models that we have been developing in our labs based on the domain-general theory of state feedback control (feedback aware control of tasks in speech model). Results After establishing the architecture of the models, we show that both the directions into velocities of articulators and state feedback control/feedback aware control of tasks models can replicate key behaviors related to sensory feedback in the speech motor system. Although the models agree on many points, the underlying architecture of the 2 models differs in a few key ways, leading to different predictions in certain areas. We cover key disagreements between the models to show the limits of our current understanding and point toward areas where future experimental studies can resolve these questions. Conclusions Understanding the role of sensory information in the speech motor system is critical to understanding speech motor production and sensorimotor learning in healthy speakers as well as in disordered populations. Computational models, with their concrete implementations and testable predictions, are an important tool to understand this process. Comparison of different models can highlight areas of agreement and disagreement in the field and point toward future experiments to resolve important outstanding questions about the speech motor control system.

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Year:  2019        PMID: 31465712      PMCID: PMC6813034          DOI: 10.1044/2019_JSLHR-S-CSMC7-18-0127

Source DB:  PubMed          Journal:  J Speech Lang Hear Res        ISSN: 1092-4388            Impact factor:   2.297


  158 in total

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Authors:  John F Houde; Michael I Jordan
Journal:  J Speech Lang Hear Res       Date:  2002-04       Impact factor: 2.297

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Journal:  J Neurosci       Date:  2015-09-09       Impact factor: 6.167

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4.  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

5.  Hierarchical Sequencing and Feedforward and Feedback Control Mechanisms in Speech Production: A Preliminary Approach for Modeling Normal and Disordered Speech.

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6.  Adaptation to pitch-altered feedback is independent of one's own voice pitch sensitivity.

Authors:  Razieh Alemi; Alexandre Lehmann; Mickael L D Deroche
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7.  Speech compensation responses and sensorimotor adaptation to formant feedback perturbations.

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Journal:  J Acoust Soc Am       Date:  2021-02       Impact factor: 1.840

8.  Inter-Trial Formant Variability in Speech Production Is Actively Controlled but Does Not Affect Subsequent Adaptation to a Predictable Formant Perturbation.

Authors:  Hantao Wang; Ludo Max
Journal:  Front Hum Neurosci       Date:  2022-07-07       Impact factor: 3.473

9.  A Computational Model for Estimating the Speech Motor System's Sensitivity to Auditory Prediction Errors.

Authors:  Ayoub Daliri
Journal:  J Speech Lang Hear Res       Date:  2021-05-27       Impact factor: 2.297

  9 in total

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