MétaCan
Menu
Back to cohort
Record W2772325037 · doi:10.1044/2017_jslhr-s-17-0075

Sentence-Level Movements in Parkinson's Disease: Loud, Clear, and Slow Speech

2017· article· en· W2772325037 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2017
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsSunnybrook Health Science CentreYork UniversityToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsIntelligibility (philosophy)SentenceTongueAudiologyPsychologySpeech productionMovement (music)Speech recognitionComputer scienceAcousticsLinguisticsMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose: To further understand the effect of Parkinson's disease (PD) on articulatory movements in speech and to expand our knowledge of therapeutic treatment strategies, this study examined movements of the jaw, tongue blade, and tongue dorsum during sentence production with respect to speech intelligibility and compared the effect of varying speaking styles on these articulatory movements. Method: Twenty-one speakers with PD and 20 healthy controls produced 3 sentences under normal, loud, clear, and slow speaking conditions. Speech intelligibility was rated for each speaker. A 3-dimensional electromagnetic articulograph tracked movements of the articulators. Measures included articulatory working spaces, ranges along the first principal component, average speeds, and sentence durations. Results: Speakers with PD demonstrated significantly smaller jaw movements as well as shorter than normal sentence durations. Between-speaker variation in movement size of the jaw, tongue blade, and tongue dorsum was associated with speech intelligibility. Analysis of speaking conditions revealed similar patterns of change in movement measures across groups and articulators: larger than normal movement sizes and faster speeds for loud speech, increased movement sizes for clear speech, and larger than normal movement sizes and slower speeds for slow speech. Conclusions: Sentence-level measures of articulatory movements are sensitive to both disease-related changes in PD and speaking-style manipulations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.404
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it