Influence of clear speech on the word intelligibility of electrolaryngeal speakers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".