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Record W2737806601 · doi:10.1159/000470905

Maintaining Distinctiveness at Increased Speaking Rates: A Comparison between Congenitally Blind and Sighted Speakers

2016· article· en· W2737806601 on OpenAlexafffundabout
Lucie Ménard, Dominique Côté, Paméla Trudeau-Fisette

Bibliographic record

VenueFolia Phoniatrica et Logopaedica · 2016
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsCentre for Research on Brain Language and MusicUniversité du Québec à Montréal
FundersLuonnontieteiden ja Tekniikan Tutkimuksen ToimikuntaNatural Sciences and Engineering Research Council of Canada
KeywordsVowelAudiologyPsychologyOptimal distinctiveness theoryContrast (vision)Articulation (sociology)Speech recognitionMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: The effects of increased speaking rates on vowels have been well documented in sighted adults. It has been reported that in fast speech, vowels are less widely spaced acoustically than in their citation form. Vowel space compression has also been reported in congenitally blind speakers. The objective of the study was to investigate the interaction of vision and speaking rate in adult speakers. PATIENTS AND METHODS: Contrast distances between vowels were examined in conversational and fast speech produced by 10 congenitally blind and 10 sighted French-Canadian adults. Acoustic analyses were carried out. RESULTS: Compared with the sighted speakers, in the fast speaking condition, the blind speakers produced more vowels with contrast along the height, place of articulation, and rounding features located within the auditory target regions typical of French vowels. CONCLUSION: Blind speakers relied more heavily than sighted speakers on auditory properties of vowels to maintain perceptual distinctiveness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.318
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueFolia Phoniatrica et LogopaedicaSame topicVoice and Speech DisordersFrench-language works237,207