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Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias

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Title: Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias
Language: English
Authors: Illner, Vojtech, Tykalová, Tereza, Novotny, Michal, Klempír, Jirí, Dušek, Petr, Rusz, Jan (ORCID 0000-0002-1036-3054)
Source: Journal of Speech, Language, and Hearing Research. Apr 2022 65(4):1386-1401.
Availability: 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
Peer Reviewed: Y
Page Count: 16
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Descriptors: Articulation (Speech), Speech Communication, Articulation Impairments, Neurological Impairments, Patients, Eye Movements, Sleep, Automation, Computer Software, Severity (of Disability), Clinical Diagnosis
DOI: 10.1044/2021_JSLHR-21-00549
ISSN: 1092-4388
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.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1343136
Database: ERIC
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  Data: 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
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  Data: 10.1044/2021_JSLHR-21-00549
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  Data: 1092-4388
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  Data: 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&#39;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 &amp; Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r &gt; 0.87 for monologues and r &gt; 0.91 for reading passages, p &lt; 0.001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r &gt; 0.65 for monologues and r &gt; 0.78 for reading passages, p &lt; 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.
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      – SubjectFull: Articulation (Speech)
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