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Record W2511410146

Speech in ALS: Longitudinal Changes in Lips and Jaw Movements and Vowel Acoustics.

2013· article· en· W2511410146 on OpenAlexaff
Yana Yunusova, Jordan R. Green, Mary J. Lindstrom, Gary L. Pattee, Lorne Zinman

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsFormantVowelKinematicsAudiologyIntelligibility (philosophy)Amyotrophic lateral sclerosisAcousticsPsychologySpeech recognitionMedicineComputer sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: The goal of this exploratory study was to investigate longitudinally the changes in facial kinematics, vowel formant frequencies, and speech intelligibility in individuals diagnosed with bulbar amyotrophic lateral sclerosis (ALS). This study was motivated by the need to understand articulatory and acoustic changes with disease progression and their subsequent effect on deterioration of speech in ALS. METHOD: Lip and jaw movements and vowel acoustics were obtained for four individuals with bulbar ALS during four consecutive recording sessions with an average interval of three months between recordings. Participants read target words embedded into sentences at a comfortable speaking rate. Maximum vertical and horizontal mouth opening and maximum jaw displacements were obtained during corner vowels. First and second formant frequencies were measured for each vowel. Speech intelligibility and speaking rate score were obtained for each session as well. RESULTS: Transient, non-vowel-specific changes in kinematics of the jaw and lips were observed. Kinematic changes often preceded changes in vowel acoustics and speech intelligibility. CONCLUSIONS: Nonlinear changes in speech kinematics should be considered in evaluation of the disease effects on jaw and lip musculature. Kinematic measures might be most suitable for early detection of changes associated with bulbar ALS.

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)0.001
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 score0.100
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.063
GPT teacher head0.349
Teacher spread0.286 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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