How Long Does It Take for a Voice to Become Familiar? Speech Intelligibility and Voice Recognition Are Differentially Sensitive to Voice Training
Bibliographic record
Abstract
When people listen to speech in noisy places, they can understand more words spoken by someone familiar, such as a friend or partner, than someone unfamiliar. Yet we know little about how voice familiarity develops over time. We exposed participants ( N = 50) to three voices for different lengths of time (speaking 88, 166, or 478 sentences during familiarization and training). These previously heard voices were recognizable and more intelligible when presented with a competing talker than novel voices—even the voice previously heard for the shortest duration. However, recognition and intelligibility improved at different rates with longer exposures. Whereas recognition was similar for all previously heard voices, intelligibility was best for the voice that had been heard most extensively. The speech-intelligibility benefit for the most extensively heard voice (10%–15%) is as large as that reported for voices that are naturally very familiar (friends and spouses)—demonstrating that the intelligibility of a voice can be improved substantially after only an hour of training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".