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Record W4323041845 · doi:10.1121/10.0017438

Reduction of vowel space in Alzheimer's disease

2023· article· en· W4323041845 on OpenAlexafffund
Arian Shamei, Yadong Liu, Bryan Gick

Bibliographic record

VenueJASA Express Letters · 2023
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication DisordersNatural Sciences and Engineering Research Council of Canada
KeywordsVowelSpace (punctuation)Speech recognitionReduction (mathematics)Computer scienceAudiologyTask (project management)Alzheimer's diseaseDiseasePsychologyArtificial intelligenceMedicineMathematicsPathologyEngineering

Abstract

fetched live from OpenAlex

Reduced vowel space area (VSA) is a known effect of neurodegenerative diseases such as Parkinson's disease (PD). Using large publicly available corpuses, two experiments were conducted comparing the vowel space of speakers with and without Alzheimer's disease (AD) during spontaneous and read speech. First, a comparison of vowel distance found reduced distance in AD for English spontaneous speech, but not Spanish read speech. Findings were then verified using an unsupervised learning approach to quantify VSA through cluster center detection. These results corroborate observations for PD that VSA reduction is task-dependent, but further experiments are necessary to quantify the effect of language.

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 score0.839
Threshold uncertainty score0.353

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 head0.023
GPT teacher head0.282
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

Citations4
Published2023
Admission routes2
Has abstractyes

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