The Influence of Stimulus Composition and Scoring Method on Objective Listener Assessments of Tracheoesophageal Speech Accuracy
Bibliographic record
Abstract
Introduction: This study investigated the influence of stimulus composition for three speech intelligibility word lists and two scoring methods on the speech accuracy judgments of five tracheoesophageal (TE) speakers. This was achieved through phonemic comparisons across TE speakers’ productions of stimuli from the three intelligibility word lists, including the (1) Consonant Rhyme Test, (2) Northwestern Intelligibility Test, and (3) the Weiss and Basili list. Methodology: Fifteen normal-hearing young adults served as listeners; all listeners were trained in phonetic transcription (IPA), but none had previous exposure to any mode of postlaryngectomy alaryngeal speech. Speaker stimuli were presented to all listeners through headphones, and all stimuli were transcribed phonetically using an open-set response paradigm. Data were analyzed for individual speakers by stimulus list. Phonemic scoring was compared to a whole-word scoring method, and the types of errors observed were quantified by word list. Results: Individual speaker variability was noted, and its effect on the assessment of speech accuracy was identified. The phonemic scoring method was found to be a more sensitive measure of TE speech accuracy. The W&B list yielded the lowest accuracy scores of the three lists. This finding may indicate its increased sensitivity and potential clinical value. Conclusions: Overall, this study supports the use of open-set, phonemic scoring methods when evaluating TE speaker intelligibility. Future research should aim to assess the specificity of assessment tools on a larger sample of TE speakers who vary in their speech proficiency.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".