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Record W2175816735 · doi:10.1121/1.4933883

Influence of clear speech on the word intelligibility of electrolaryngeal speakers

2015· article· en· W2175816735 on OpenAlexaff
Steven R. Cox, Philip C. Doyle

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2015
Typearticle
Languageen
FieldMedicine
TopicVoice and Speech Disorders
Canadian institutionsWestern University
Fundersnot available
KeywordsIntelligibility (philosophy)PsychologyLinguisticsAudiologyWord (group theory)Speech recognitionComputer scienceMedicinePhilosophy

Abstract

fetched live from OpenAlex

Electrolaryngeal (EL) speech is often used by individuals who undergo total laryngectomy (Barney, Haworth, & Dunn, 1959; Doyle, 1994). Unfortunately, current EL devices continue to produce an unnatural and mechanical sound quality that can impact speech intelligibility (SI) (Doyle & Eadie, 2005; Hillman, Walsh, Wolf, Fisher, & Hong, 1998; Meltzner & Hillman, 2005). Clear speech (CS) requires speakers to produce speech “as clearly as possible” in an attempt to improve the overall understandability of speech (Picheny, Durlach, & Braida, 1985, 1986) in those with communication disorders (Beukelman, Fager, Ullman, Hanson, & Logemann, 2002; Tjaden, Sussman, & Wilding, 2014). This study addressed alterations in SI through the application of CS by EL speakers. Twelve naïve listeners orthographically transcribed CVC words produced by 10 EL speakers in both habitual and CS modes. Findings indicate that SI improved 1.5% for words spoken using CS (M = 53.25%, range = 47 to 64%) compared to words spoken using habitual speech (M = 51.75%, range = 41 to 62%). No significant differences in SI were found in either word-initial or word-final phonemes across speaking conditions. Clinical implications of these findings will be discussed.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.283
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.284
Teacher spread0.263 · 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
Published2015
Admission routes1
Has abstractyes

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