Abstract

Purpose
As increasing amounts and types of speech data become accessible, health care and technology industries increasingly demand quantitative insight into speech content. The potential for speech data to provide insight into cognitive, affective, and psychological health states and behavior crucially depends on the ability to integrate speech data into the scientific process. Current engineering methods for acquiring, analyzing, and modeling speech data present the opportunity to integrate speech data into the scientific process. Additionally, machine learning systems recognize patterns in data that can facilitate hypothesis generation, data analysis, and statistical modeling. The goals of the present article are (a) to review developments across these domains that have allowed real-time magnetic resonance imaging to shed light on aspects of atypical speech articulation; (b) in a parallel vein, to discuss how …

Date

2019

Authors

Christina Hagedorn, Tanner Sorensen, Adam Lammert, Asterios Toutios, Louis Goldstein, Dani Byrd, Shrikanth Narayanan

Journal

Perspectives of the ASHA Special Interest Groups

Volume

4

Issue

2

Pages

411-420

Publisher

American Speech-Language-Hearing Association