Vocal Tract Representation in the Recognition of Cerebral Palsied Speech.
Purpose: In this study, the authors explored articulatory information as a means of improving the recognition of dysarthric speech by machine. Method: Data were derived chiefly from the TORGO database of dysarthric articulation (Rudzicz, Namasivayam, & Wolff, 2011) in which motions of various points...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 55; no. 4; pp. 1190 - 1208 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
American Speech-Language-Hearing Association
8/1/2012
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=78951128&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 78951128 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: 8/1/2012 vid: 55 iid: 4 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 78951128 10.1044/1092-4388(2011/11-0223) ppf: 1190 ppct: 18 formats: fmt: @attributes: type: P size: 323KB tig: atl: Vocal Tract Representation in the Recognition of Cerebral Palsied Speech. aug: au: Rudzicz, Frank Hirst, Graeme Lieshout, Pascal Van Smith, Anne Ziegler, Wolfram affil: University of Toronto, Ontario, Canada Institute of Biomaterials and Biomedical Engineering, Toronto Toronto Rehabilitation Institute su: Phonetics Recognition (Psychology) Articulation disorders Cerebral palsy Experimental design Noise Regression analysis Research funding Physiological aspects of speech Speech perception Vocal cords Maximum likelihood statistics sug: subj: Phonetics Recognition (Psychology) Articulation disorders Cerebral palsy Experimental design Noise Regression analysis Research funding Physiological aspects of speech Speech perception Vocal cords Maximum likelihood statistics keyword: articulation dysarthria speech recognition articulation dysarthria speech recognition ab: Purpose: In this study, the authors explored articulatory information as a means of improving the recognition of dysarthric speech by machine. Method: Data were derived chiefly from the TORGO database of dysarthric articulation (Rudzicz, Namasivayam, & Wolff, 2011) in which motions of various points in the vocal tract are measured during speech. In the 1st experiment, the authors provided a baseline model indicating a relatively low performance with traditional automatic speech recognition (ASR) using only acoustic data from dysarthric individuals. In the 2nd experiment, the authors used various measures of entropy (statistical disorder) to determine whether characteristics of dysarthric articulation can reduce uncertainty in features of dysarthric acoustics. These findings led to the 3rd experiment, in which recorded dysarthric articulation was directly encoded into the speech recognition process. Results: The authors found that 18.3% of the statistical disorder in the acoustics of speakers with dysarthria can be removed if articulatory parameters are known. Using articulatory models reduces phoneme recognition errors relatively by up to 6% for speakers with dysarthria in speaker-dependent systems. Conclusions: Articulatory knowledge is useful in reducing rates of error in ASR for speakers with dysarthria and in reducing statistical uncertainty of their acoustic signals. These findings may help to guide clinical decisions related to the use of ASR in the future. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|