Engineering Innovation in Speech Science: Data and Technologies.
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 cruc...
| Publicado en: | Perspectives of the ASHA Special Interest Groups Vol. 4; no. 2; pp. 411 - 421 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images pictorial review tables/charts tracings Journal Article |
| Publicado: |
American Speech-Language-Hearing Association
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136011024&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136011024 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2381473X KTSD jtl: Perspectives of the ASHA Special Interest Groups issn: 2381473X maglogo: N pubinfo: dt: 2019 vid: 4 iid: 2 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 136011024 136011024 136011024 10.1044/2018_PERS-SIG19-2018-0003 136011024 ppf: 411 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Engineering Innovation in Speech Science: Data and Technologies. aug: au: Hagedorn, Christina Sorensen, Tanner Lammert, Adam Toutios, Asterios Goldstein, Louis Byrd, Dani Narayanan, Shrikanth affil: Linguistics, College of Staten Island, City University of New York, NY sug: subj: Speech Engineering Technology Machine Learning Signal Processing, Computer Assisted Communication Cognition Affect Psychological Well-Being Behavior Hypothesis Data Analysis Data Management Speech Articulation Tests Collaboration Scientists Speech-Language Pathologists Data Science American Speech-Language-Hearing Association Autism Spectrum Disorder Magnetic Resonance Imaging Phonetics Speech Production Measurement Apraxia Nonverbal Communication Interpersonal Relations Articulation Disorders ab: 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. Supplemental Material: https://doi.org/10.23641/asha.7740191 pubtype: Academic Journal doctype: diagnostic images pictorial review tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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