Adaptation of the Connected Speech Test: Rerecording and Passage Equivalency
Bibliographic record
Abstract
Purpose The original Connected Speech Test (CST; Cox et al., 1987) is a well-regarded and often utilized speech perception test. The aim of this study was to develop a new version of the CST using a neutral North American accent and to assess the use of this updated CST on participants with normal hearing. Method A female English speaker was recruited to read the original CST passages, which were recorded as the new CST stimuli. A study was designed to assess the newly recorded CST passages' equivalence and conduct normalization. The study included 19 Western University students (11 females and eight males) with normal hearing and with English as a first language. Results Raw scores for the 48 tested passages were converted to rationalized arcsine units, and average passage scores more than 1 rationalized arcsine unit standard deviation from the mean were excluded. The internal reliability of the 32 remaining passages was assessed, and the two-way random effects intraclass correlation was .944. Conclusion The aim of our study was to create new CST stimuli with a more general North American accent in order to minimize accent effects on the speech perception scores. The study resulted in 32 passages of equivalent difficulty for listeners with normal hearing.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".