Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias

Autor/inn/en	Illner, Vojtech; Tykalová, Tereza; Novotny, Michal; Klempír, Jirí; Dušek, Petr; Rusz, Jan
Titel	Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias
Quelle	In: Journal of Speech, Language, and Hearing Research, 65 (2022) 4, S.1386-1401 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Rusz, Jan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Articulation (Speech); Speech Communication; Articulation Impairments; Neurological Impairments; Patients; Eye Movements; Sleep; Automation; Computer Software; Severity (of Disability); Clinical Diagnosis + Suchen Sie Ihr Suchwort? Artikulationsstörung; Neurodegenerative Erkrankung; Patient; Augenbewegung; Schlaf; Schweregrad
Abstract	Purpose: This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls. Method: Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their performances were compared to a manual transcript used as a reference. Results: The Google & Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r > 0.87 for monologues and r > 0.91 for reading passages, p < 0.001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r > 0.65 for monologues and r > 0.78 for reading passages, p < 0.001). Compared to the control group, we detected a slow rate in patients with MSA and a tendency toward a slower rate in patients with iRBD, whereas the articulation rate was unchanged in patients with early untreated PD. Conclusions: The state-of-the-art speech recognition tool provided the most precise articulation rate estimates. If speech recognizer is not accessible, the freely available Praat script based on simple intensity thresholding might still provide robust properties even in severe dysarthria. Automated articulation rate assessment may serve as a natural, inexpensive biomarker for monitoring disease severity and a differential diagnosis of Parkinsonism. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01