Dataset Information

Using Crowdsourced Listeners' Ratings to Measure Speech Changes in Hypokinetic Dysarthria: A Proof-of-Concept Study.

ABSTRACT: Purpose Interventions for speech disorders aim to produce changes that are not only acoustically measurable or perceptible to trained professionals but are also apparent to naive listeners. Due to challenges associated with obtaining ratings from suitably large listener samples, however, few studies currently evaluate speech interventions by this criterion. Online crowdsourcing technologies could enhance the measurement of intervention effects by making it easier to obtain real-world listeners' ratings. Method Stimuli, drawn from a published study by Sapir et al. ("Effects of intensive voice treatment (Lee Silverman Voice Treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: Acoustic and perceptual findings" in Journal of Speech, Language, and Hearing Research, 50(4), 2007), were words produced by individuals who received intensive treatment (LSVT LOUD) for hypokinetic dysarthria secondary to Parkinson's disease. Thirty-six online naive listeners heard randomly ordered pairs of words elicited pre- and posttreatment and reported which they perceived as "more clearly articulated." Results Mixed-effects logistic regression indicated that words elicited posttreatment were significantly more likely to be rated "more clear." Across individuals, acoustically measured magnitude of change was significantly correlated with pre-post difference in listener ratings. Conclusions These results partly replicate the findings of Sapir et al. (2007) and demonstrate that their acoustically measured changes are detectable by everyday listeners. This supports the viability of using crowdsourcing to obtain more functionally relevant measures of change in clinical speech samples. Supplemental Material https://doi.org/10.23641/asha.12170112.

SUBMITTER: Nightingale C

PROVIDER: S-EPMC7842862 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Using Crowdsourced Listeners' Ratings to Measure Speech Changes in Hypokinetic Dysarthria: A Proof-of-Concept Study.

Nightingale Christopher C Swartz Michelle M Ramig Lorraine Olson LO McAllister Tara T

American journal of speech-language pathology 20200424 2

Purpose Interventions for speech disorders aim to produce changes that are not only acoustically measurable or perceptible to trained professionals but are also apparent to naive listeners. Due to challenges associated with obtaining ratings from suitably large listener samples, however, few studies currently evaluate speech interventions by this criterion. Online crowdsourcing technologies could enhance the measurement of intervention effects by making it easier to obtain real-world listeners' ...[more]

PMID: 32331503

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Project description:ImportanceMortality across US counties varies considerably, from 252 to 1847 deaths per 100 000 people in 2018. Although patient satisfaction with health care is associated with patient- and facility-level health outcomes, the association between health care satisfaction and community-level health outcomes is not known.ObjectiveTo examine the association between online ratings of health care facilities and mortality across US counties and to identify language specific to 1-star (lowest rating) and 5-star (highest rating) reviews in counties with high vs low mortality.Design, setting, and participantsThis retrospective population-based cross-sectional study examined reviews and ratings of 95 120 essential health care facilities across 1301 US counties. Counties that had at least 1 essential health care facility with reviews available on Yelp, an online review platform, were included. Essential health care was defined according to the 10 essential health benefits covered by Affordable Care Act insurance plans.Main outcomes and measuresThe mean rating of essential health care facilities was calculated by county from January 1, 2015, to December 31, 2019. Ratings were on a scale of 1 to 5 stars, with 1 being the worst rating and 5 the best. County-level composite measures of health behaviors, clinical care, social and economic factors, and physical environment were obtained from the University of Wisconsin School of Medicine and Public Health County Health Rankings database. The 2018 age-adjusted mortality by county was obtained from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiological Research database. Multiple linear regression analysis was used to estimate the association between mean facility rating and mortality, adjusting for county health ranking variables. Words with frequencies of use that were significantly different across 1-star and 5-star reviews in counties with high vs low mortality were identified.ResultsThe 95 120 facilities meeting inclusion criteria were distributed across 1301 of 3142 US counties (41.4%). At the county level, a 1-point increase in mean rating was associated with a mean (SE) age-adjusted decrease of 18.05 (3.68) deaths per 100 000 people (P < .001). Words specific to 1-star reviews in high-mortality counties included told, rude, and wait, and words specific to 5-star reviews in low-mortality counties included Dr, pain, and professional.Conclusions and relevanceThis study found that, at the county level, higher online ratings of essential health care facilities were associated with lower mortality. Equivalent online ratings did not necessarily reflect equivalent experiences of care across counties with different mortality levels, as evidenced by variations in the frequency of use of key words in reviews. These findings suggest that online ratings and reviews may provide insight into unequal experiences of essential health care.

Project description:Dysarthria may present during the natural course of many degenerative neurological conditions. Hypokinetic and ataxic dysarthria are common in movement disorders and represent the underlying neuropathology. We developed an artificial intelligence (AI) model to distinguish ataxic dysarthria and hypokinetic dysarthria from normal speech and differentiate ataxic and hypokinetic speech in parkinsonian diseases and cerebellar ataxia. We screened 804 perceptual speech analyses performed in the Samsung Medical Center Neurology Department between January 2017 and December 2020. The data of patients diagnosed with parkinsonian disorders or cerebellar ataxia were included. Two speech tasks (numbering from 1 to 50 and reading nine sentences) were analyzed. We adopted convolutional neural networks and developed a patch-wise wave splitting and integrating AI system for audio classification (PWSI-AI-AC) to differentiate between ataxic and hypokinetic speech. Of the 395 speech recordings for the reading task, 76, 112, and 207 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. Of the 409 recordings of the numbering task, 82, 111, and 216 were from normal, ataxic dysarthria, and hypokinetic dysarthria subjects, respectively. The reading and numbering task recordings were classified with 5-fold cross-validation using PWSI-AI-AC as follows: hypokinetic dysarthria vs. others (area under the curve: 0.92 ± 0.01 and 0.92 ± 0.02), ataxia vs. others (0.93 ± 0.04 and 0.89 ± 0.02), hypokinetic dysarthria vs. ataxia (0.96 ± 0.02 and 0.95 ± 0.01), hypokinetic dysarthria vs. none (0.86 ± 0.03 and 0.87 ± 0.05), and ataxia vs. none (0.87 ± 0.07 and 0.87 ± 0.09), respectively. PWSI-AI-AC showed reliable performance in differentiating ataxic and hypokinetic dysarthria and effectively augmented data to classify the types even with limited training samples. The proposed fully automatic AI system outperforms neurology residents. Our model can provide effective guidelines for screening related diseases and differential diagnosis of neurodegenerative diseases.

Dataset Information

Using Crowdsourced Listeners' Ratings to Measure Speech Changes in Hypokinetic Dysarthria: A Proof-of-Concept Study.

Publications

Using Crowdsourced Listeners' Ratings to Measure Speech Changes in Hypokinetic Dysarthria: A Proof-of-Concept Study.

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