Machine classification of prosodic control in dysarthria. (Report)
Recent studies suggest that speakers with dysarthria may be able to manipulate prosodic features sufficiently to convey information. Leveraging prosodic cues as an alternative or augmentative communication (AAC) signal may allow some individuals with dysarthria to use their vocalizations to engage i...
| Publicado en: | Journal of Medical Speech-Language Pathology Vol. 18; no. 4; pp. 4p - 5 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Cengage Delmar Learning
2010 Dec
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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=104810046&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104810046 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10651438 H23 jtl: Journal of Medical Speech-Language Pathology issn: 10651438 maglogo: N pubinfo: dt: 2010 Dec vid: 18 iid: 4 pid: 79059 pub: Cengage Delmar Learning place: Brandon, Mississippi artinfo: ui: 104810046 104810046 2010933844 104810046 ppf: 4p ppct: 1 formats: tig: atl: Machine classification of prosodic control in dysarthria. (Report) aug: au: DiCicco, Thomas M. Patel, Rupal affil: Harvard-MIT Division of Health Sciences & Technology -- Program in Speech and Hearing Bioscience and Technology, Cambridge sug: subj: Alternative and Augmentative Communication Utilization Control (Psychology) Dysarthria Classification Speech Acoustics Classification Algorithms Utilization Audiorecording Comparative Studies Databases Utilization Funding Source Human Motor Skills Time Factors ab: Recent studies suggest that speakers with dysarthria may be able to manipulate prosodic features sufficiently to convey information. Leveraging prosodic cues as an alternative or augmentative communication (AAC) signal may allow some individuals with dysarthria to use their vocalizations to engage in richer and more efficient interactions. As an initial step towards building voice-driven communication aids, the performance of three machine classification algorithms was compared to determine which algorithm(s) was most accurate and efficient for classifying a dataset of prosodic manipulations. Our findings suggest that machine classification of dysarthric productions is feasible using preexisting machine learners and rather minimal training data. Highly accurate classification of categorical duration control was achieved for all speakers with dysarthria; however, classification of pitch categories and simultaneous duration-pitch control varied widely across speakers. These findings have implications for harnessing the residual vocal abilities of individuals with dysarthria for machine-mediated AAC interactions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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