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Record W2769740949 · doi:10.1121/1.5014677

Articulatory kinematics during stop closure in speakers with Parkinson’s Disease

2017· article· en· W2769740949 on OpenAlexaboutno aff
Austin Thompson, Amanda Kuylen, Yunjung Kim

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyTongueInterval (graph theory)Speech productionKinematicsIntelligibility (philosophy)PsychologyAcousticsMathematicsMedicineSpeech recognitionComputer sciencePhysics

Abstract

fetched live from OpenAlex

Numerous studies have identified the perceptual characteristics of speakers with Parkinson’s disease (PD), imprecise consonants (e.g., Darley, Aronson, and Brown, 1969). Acoustic studies have supported these findings with the observations such as spirantization, or the incomplete closure and fricative-like production of aperiodic noise during the closuresilent interval of stop consonants (Weismer, Yunusova, and Bunton, 2012). The current presentation explores the articulatory kinematics in speakers with PD during the closure interval duration of stop consonants with respect to distance, displacement, and timing of inter-articulators motion. In addition, the changes in articulatory movements during this brief time interval are examined as the speakers voluntarily vary the degree of speech intelligibility. Participants with PD and neurologically healthy controls were asked to read sentences containing stop consonants (e.g.,“Buy Bobby a puppy”). Movement data were collected using the WAVE (NDI, Canada). The results from the five articulatory measurement points (tongue front, tongue back, upper lip, lower lip, and jaw) will be presented with emphasis on segment-specific movement characteristics of individuals with Parkinson’s disease, PD.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
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 score0.116
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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