Normative Performance Functions for the Modified Connected Speech Test
Bibliographic record
Abstract
PURPOSE: The Connected Speech Test (CST) assesses an individual's ability to understand everyday contextualized running speech amidst competing background babble. To minimize accent effects on speech perception scores and reduce the noise floor of the original recordings, an updated version was developed by Saleh et al. (2020). The updated recordings feature a speaker with a General American accent to replace the Southern U.S. accent in the original test, and modern recording equipment was used to achieve a lower noise floor. The aim of this study was to collect normative data, characterizing performance on the updated CST. Self-reported speech intelligibility and listening effort ratings were collected to examine how subjective perceptions of the task vary across test conditions. METHOD: To evaluate normative performance on this updated test, 40 native English-speaking adults (36 females and four males) with normal hearing were recruited from The University of Western Ontario. Multitalker babble was presented at a fixed level, and speech was presented at fixed signal-to-babble ratios (SBRs) to participants in both co-located and separated loudspeaker conditions. At each SBR, participants were scored based on key words correctly identified. Subjective speech intelligibility and listening effort were evaluated using self-report scales. For each measure, data were fitted with transfer functions to characterize performance on the task. RESULTS: Participants demonstrated significantly better performance in the separated loudspeaker condition, indicating a spatial release from masking. For both conditions, increased SBR was associated with increased performance and subjective speech intelligibility, and decreased listening effort. CONCLUSION: The study provides normative data to characterize expected performance for the updated version of the CST.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".