Effects of noise, native language, age, and speaker gender on intelligibility in a large corpus of read speech
Bibliographic record
Abstract
Because speech communication takes place under noisy conditions, investigating the effect of noise on intelligibility is an essential part of understanding speech perception. One challenge is having sufficient numbers of speakers and unique sentences to control for sentence and speaker effects. In an online experiment, we used a corpus of 720 IEEE sentences read by 20 native English speakers (10 male, 10 female) from the Pacific Northwest (WA, OR, ID), which were embedded in three corpus-shaped noise conditions (−2, 0, + 2 dB). Stimuli were presented to undergraduate students at the Universities of Alberta and Washington using a design matrix which ensured that no listener heard a sentence more than once. Participants entered responses into a form which appeared immediately after the stimulus completed playing. Data collection is ongoing, but the current number of listeners is 1269, who responded to 120 items each. We use Levenshtein and Jaro distance measures to compare listener transcription accuracy. We investigate the effects of listener native language, age, and gender. We also investigate effects of the speaker’s gender on transcription accuracy. [Work supported by NIH NIDCD R01 DC006014.]
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".