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R01NIH · NATIONAL INSTITUTE ON DEAFNESS AND OTHER COMMUNICATION DISORDERSNIH

Improving speech motor learning processes using augmented behavioral interventions

Daliri, Ayoub (Contact)·ARIZONA STATE UNIVERSITY-TEMPE CAMPUS, AZ·2022–2027·ACTIVE
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INSTITUTION

ARIZONA STATE UNIVERSITY-TEMPE CAMPUS, AZ

PRINCIPAL INVESTIGATOR

Daliri, Ayoub (Contact)

FUNDING

$364K

YEAR

2022

MOONBASE SCORE

Still being scored

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Abstract

PROJECT SUMMARY / ABSTRACT Speech disorders are the most prevalent form of communication disorders. Many treatments of speech disorders involve learning new speech behaviors or modifying abnormal speech behaviors. These treatments strongly rely on processes of speech motor learning—improvement in motor performance through practice. However, speech disorders are often associated with impairments in various speech motor learning processes, resulting in inefficient or deficient speech motor learning. Inefficiencies in speech motor learning processes reduce the effectiveness of the treatments that rely on these learning processes. Therefore, there is a critical need (1) to understand the specific contributions of each of the processes of speech motor learning and (2) to develop behavioral protocols that selectively influence various motor learning processes to improve speech motor performance. Without this knowledge, the promise of developing effective and optimized treatments for speech disorders will likely remain limited. This proposal’s overall objective is to develop behavioral protocols that selectively target and improve speech motor learning processes in healthy adults. Here, we propose a research program to develop and optimize a set of visually augmented training protocols to improve the accuracy of two processes crucial for successful speech motor learning: auditory error detection and auditory-to-motor mapping. Our central hypothesis is that improving these processes through augmented training can improve speech motor learning. We formulated this hypothesis based on current theoretical models of speech, including our recent computational model. Aim 1 will evaluate the effects of error-detection training on speech motor learning. Using visual feedback and auditory feedback perturbation, we will train subjects to detect and estimate auditory errors more accurately. Subsequently, we will evaluate subjects’ extent of speech motor learning. Aim 2 will determine the contributions of auditory-to-motor mapping to speech motor learning. Using visual feedback, we will train subjects to learn the relationship between various articulatory configurations and their auditory consequences. Then, we will evaluate subjects’ extent of speech motor learning. This project’s results will have a critical positive impact because (1) they will form a strong scientific foundation that can inform the development of effective and optimized treatments for speech disorders, and (2) they will have significant theoretical implications by elucidating processes of speech motor learning.

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