posted on 2019-02-22, 22:03authored byChristina Hagedorn, Tanner Sorensen, Adam Lammert, Asterios Toutios, Louis Goldstein, Shrikanth Narayanan
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 advancements in signal processing have allowed for an improved understanding of communication markers associated with autism spectrum disorder; and (c) to highlight the clinical significance and implications of the application of these technological advancements to each of these areas.
Conclusion: The collaboration of engineers, speech scientists, and clinicians has resulted in (a) the development of biologically inspired technology that has been proven useful for both small- and large-scale analyses, (b) a deepened practical and theoretical understanding of both typical and impaired speech production, and (c) the establishment and enhancement of diagnostic and therapeutic tools, all having far-reaching, interdisciplinary significance.
Research was supported in part by the National Institute on Deafness and Other Communication Disorders Grants R01 DC007124-01 and 1514544 from the National Science Foundation and from the Simons Foundation.