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Record W4385423215 · doi:10.1080/2050571x.2023.2241216

Effect of speech rate and complexity on sentence comprehension in Alzheimer’s disease

2023· article· en· W4385423215 on OpenAlexaff
Jeff Small, Diana Cochrane

Bibliographic record

VenueSpeech Language and Hearing · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComprehensionSentencePhraseReplicatePsychologyLinguisticsMeaning (existential)Cognitive psychologyComputer scienceSpeech recognitionNatural language processingMathematics

Abstract

fetched live from OpenAlex

Previous research has not shown a benefit of slowed speech on the comprehension of sentences by persons with Alzheimer’s disease (AD). The objective of this study was to replicate and extend the findings from previous research by employing a novel speech rate manipulation that inserted strategic pauses at phrase and clause boundaries in sentences. Fourteen participants with AD were instructed to match auditorily presented sentences to one of several pictures that corresponded to the correct meaning of each sentence. The sentences varied in their speech rate and grammatical complexity. The results show that participants’ comprehension did not significantly benefit from the altered speech rate, though participants did demonstrate better comprehension of simpler than more complex sentences. The findings extend previous research by showing that even when employing a more natural method of slowing the speech signal it did not benefit AD participants’ comprehension. The results also contribute to evidence-based clinical recommendations concerning speech modifications to facilitate verbal comprehension in Alzheimer’s disease.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.452

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 head0.074
GPT teacher head0.343
Teacher spread0.269 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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