Purpose: Limited research has examined the suitability of crowdsourced ratings to measure treatment effects in speakers with Parkinson's disease (PD), particularly for constructs such as voice quality. This study obtained measures of reliability and validity for crowdsourced listeners' ratings of voice quality in speech samples from a published study. We also investigated whether aggregated listener ratings would replicate the original study's findings of treatment effects based on the Acoustic Voice Quality Index (AVQI) measure.
Method: This study reports a secondary outcome measure of a randomized controlled trial with speakers with dysarthria associated with PD, including two active comparators (Lee Silverman Voice Treatment [LSVT LOUD] and LSVT ARTIC), an inactive comparator (untreated PD), and a healthy control group. Speech samples from three time points (pretreatment, posttreatment, and 6-month follow-up) were presented in random order for rating as "typical" or "atypical" with respect to voice quality. Untrained listeners were recruited through the Amazon Mechanical Turk crowdsourcing platform until each sample had at least 25 ratings.
Results: Intrarater reliability for tokens presented repeatedly was substantial (Cohen's κ = .65-.70), and interrater agreement significantly exceeded chance level. There was a significant correlation of moderate magnitude between the AVQI and the proportion of listeners classifying a given sample as "typical." Consistent with the original study, we found a significant interaction between group and time point, with the LSVT LOUD group alone showing significantly higher perceptually rated voice quality at posttreatment and follow-up relative to the pretreatment time point.
Conclusions: These results suggest that crowdsourcing can be a valid means to evaluate clinical speech samples, even for less familiar constructs such as voice quality. The findings also replicate the results of the study by Moya-Galé et al. (2022) and support their functional relevance by demonstrating that the effects of treatment measured acoustically in that study are perceptually apparent to everyday listeners.
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http://dx.doi.org/10.1044/2023_JSLHR-22-00694 | DOI Listing |
Clin Linguist Phon
January 2025
École d'orthophonie et d'audiologie, Faculté de médecine, Université de Montréal, Québec, Canada.
This article presents the Quebec French adaptation of the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V), a standardised protocol for evaluating voice quality. Developed through collaboration within the Quebec Voice Speech-Language Pathologist (SLP) Community of Practice, the adapted tool addresses linguistic and cultural nuances specific to Quebec French. This adaptation ensures standardised assessments and harmonises clinical and research practices across the province.
View Article and Find Full Text PDFInt J Chron Obstruct Pulmon Dis
January 2025
Department of Cardiology, Respiratory Medicine and Intensive Care, University Hospital Augsburg, Augsburg, Germany.
Background: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis of speech for classifying the disease severity of COPD.
Methods: In this single centre study, non-consecutive COPD patients were prospectively recruited for comparing their speech characteristics during and after an acute COPD exacerbation.
Digit Health
January 2025
Independent Researcher, Calgary, Alberta, Canada.
Digital health (DH) and artificial intelligence (AI) in healthcare are rapidly evolving but were addressed synonymously by many healthcare authorities and practitioners. A deep understanding and clarification of these concepts are fundamental and a prerequisite for developing robust frameworks and practical guidelines to ensure the safety, efficacy, and effectiveness of DH solutions and AI-embedded technologies. Categorizing DH into technologies (DHTs) and services (DHSs) enables regulatory, HTA, and reimbursement bodies to develop category-specific frameworks and guidelines for evaluating these solutions effectively.
View Article and Find Full Text PDFJ Voice
January 2025
Department of Speech and Language Therapy, School of Health Rehabilitation Sciences, University of Patras, Patras, Greece; A' ENT University Clinic, Medical School, National Kapodistreian University of Athens, Athens, Greece. Electronic address:
Objectives: The Singing Voice Handicap Index (SVHI) was culturally adapted and validated in Greek to examine the impacts of voice problems on a singer's everyday life.
Methods: The translated version was administered to 120 singers in total, along with the translated version of the Voice Handicap Index (VHI), a sort voice history questionnaire, two Self-Rating Dysphonia Severity Scales (SRDSSs), and two visual analog scales. A week after the original completion of the Greek version of SVHI, a second copy of the SVHI was administered to 50% of the participants.
Sensors (Basel)
January 2025
Instituto de Ciencias Aplicadas y Tecnología (ICAT), Universidad Nacional Autónoma de México, Ciudad de México C.P. 04510, Mexico.
Mobility is essential for individuals with physical disabilities, and wheelchairs significantly enhance their quality of life. Recent advancements focus on developing sophisticated control systems for effective and efficient interaction. This study evaluates the usability and performance of three wheelchair control modes manual, automatic, and voice controlled using a virtual reality (VR) simulation tool.
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