Production of Vowels by Electrolaryngeal Speakers Using Clear Speech
Bibliographic record
Abstract
BACKGROUND/AIMS: This study examined the effect of clear speech on vowel productions by electrolaryngeal speakers. METHOD: Ten electrolaryngeal speakers produced eighteen words containing /i/, /ɪ/, /ɛ/, /æ/, /eɪ/, and /oʊ/ using habitual speech and clear speech. Twelve listeners transcribed 360 words, and a total of 4,320 vowel stimuli across speaking conditions, speakers, and listeners were analyzed. Analyses included listeners' identifications of vowels, vowel duration, and vowel formant relationships. RESULTS: No significant effect of speaking condition was found on vowel identification. Specifically, 85.4% of the vowels were identified in habitual speech, and 82.7% of the vowels were identified in clear speech. However, clear speech was found to have a significant effect on vowel durations. The mean vowel duration in the 17 consonant-vowel-consonant words was 333 ms in habitual speech and 354 ms in clear speech. The mean vowel duration in the single consonant-vowel words was 551 ms in habitual speech and 629 ms in clear speech. CONCLUSION: Finding suggests that, although clear speech facilitates longer vowel durations, electrolaryngeal speakers may not gain a clear speech benefit relative to listeners' vowel identifications.
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 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 | 0.004 |
| 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 | 0.000 | 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".