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Discovering Dynamical Laws for Speech Gestures.

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Title: Discovering Dynamical Laws for Speech Gestures.
Authors: Kirkham, Sam1 (AUTHOR) s.kirkham@lancaster.ac.uk
Source: Cognitive Science. May2025, Vol. 49 Issue 5, p1-40. 40p.
Subject Terms: Oral communication, Speech, Dynamical systems, Differential equations, Complex variables
Abstract: A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behavior. Take the example of spoken language, which is characterized by a highly variable and complex set of physical movements that map onto the small set of cognitive units that comprise language. What are the fundamental dynamical principles behind the movements that structure speech production? In this study, we discover models in the form of symbolic equations that govern articulatory gestures during speech. A sparse symbolic regression algorithm is used to discover models from kinematic data on the tongue and lips. We explore these candidate models using analytical techniques and numerical simulations and find that a second‐order linear model achieves high levels of accuracy, but a nonlinear force is required to properly model articulatory dynamics in approximately one third of cases. This supports the proposal that an autonomous, nonlinear, second‐order differential equation is a viable dynamical law for articulatory gestures in speech. We conclude by identifying future opportunities and obstacles in data‐driven model discovery and outline prospects for discovering the dynamical principles that govern language, brain, and behavior. [ABSTRACT FROM AUTHOR]
Copyright of Cognitive Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Oral+communication%22">Oral communication</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Differential+equations%22">Differential equations</searchLink><br /><searchLink fieldCode="DE" term="%22Complex+variables%22">Complex variables</searchLink>
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  Data: A fundamental challenge in the cognitive sciences is discovering the dynamics that govern behavior. Take the example of spoken language, which is characterized by a highly variable and complex set of physical movements that map onto the small set of cognitive units that comprise language. What are the fundamental dynamical principles behind the movements that structure speech production? In this study, we discover models in the form of symbolic equations that govern articulatory gestures during speech. A sparse symbolic regression algorithm is used to discover models from kinematic data on the tongue and lips. We explore these candidate models using analytical techniques and numerical simulations and find that a second‐order linear model achieves high levels of accuracy, but a nonlinear force is required to properly model articulatory dynamics in approximately one third of cases. This supports the proposal that an autonomous, nonlinear, second‐order differential equation is a viable dynamical law for articulatory gestures in speech. We conclude by identifying future opportunities and obstacles in data‐driven model discovery and outline prospects for discovering the dynamical principles that govern language, brain, and behavior. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Cognitive Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/cogs.70064
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              Text: May2025
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