Effects of nasality and utterance length on the recognition of familiar speakers.
Bibliographic record
Abstract
The present study examines the effects of nasality and utterance length on memory of familiar speakers using the technique of voice line-ups. With this technique, presented speakers have similar speech F0, dialect, and age range, and they utter the same material. Sets of voice line-ups were elaborated each containing 10 male voices (1 target “familiar” voice and 9 “filler” voices). In each set, speakers produced given utterances of four different lengths, with varying numbers of nasal sounds. Participants (n = 44) were selected on the basis of their familiarity with the target voice. They were asked to identify the familiar voice within line-ups. The results show that both utterance length and nasality positively influence voice recognition but these effects only begin after hearing four or more syllables. This suggests that speaker recognition requires a few syllables and may not operate as quickly as processes of visual recognition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".