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Record W2087399271 · doi:10.1121/1.4805611

Compensatory articulation in amyotrophic lateral sclerosis: Tongue and jaw interactions

2013· article· en· W2087399271 on OpenAlexaff
Sanjana Shellikeri, Yana Yunusova, D. Thomas, Jordan J. Green, Lorne Zinman

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsTongueAmyotrophic lateral sclerosisMedicineDysarthriaArticulation (sociology)VowelAudiologyFormantIntelligibility (philosophy)Speech productionManner of articulationSpeech recognitionDiseaseComputer science

Abstract

fetched live from OpenAlex

Previous acoustic studies on speech deterioration in amyotrophic lateral sclerosis (ALS) demonstrated that those at more advanced stages of disease show reduced F2 (second formant) slopes presumably due to disease-related changes in the tongue. Other studies have shown that patients with ALS use their jaw to compensate for decreased tongue function in speech. However, no study to date has examined the compensatory role that the jaw has on maintaining the acoustic signatures of vocalic segments. This study will report F2 slope differences in vowels and diphthongs produced with and without jaw stabilization via a bite block. Based on previous studies, I hypothesized that the bite block will affect F2 slope measures in individuals with significant tongue impairment on the oral-motor examination and low speech intelligibility scores. Thirty participants repeat a carrier phrase “Say _ again” with words “wax, sip, yo-yo, sight” three times with and without the bite block. Kinematic measures of the distance, time, and speed, and acoustic measures of F2 slope are reported. The data will be discussed in the context of the role of the tongue in vowel production.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.158

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 studies0.0000.000
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 head0.021
GPT teacher head0.281
Teacher spread0.259 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicNeurogenetic and Muscular Disorders ResearchFrench-language works237,207