When do which sounds tell you who says what? A phonetic investigation of the familiar talker advantage in word recognition
Bibliographic record
Abstract
This study investigated whether voice quality, which is a highly salient cue to talker identity, facilitates the process of word recognition for familiar talkers. Two groups of listeners were trained to identify the same set of talkers. One group of listeners heard these talkers speaking in a variety of voice qualities (modal, creaky, and breathy), while another group of listeners heard each talker speaking in only one voice quality. Both groups of listeners were then tested on their ability to identify words spoken by these familiar talkers—in a variety of voice qualities—and also words spoken by a group of unfamiliar talkers. Results showed that voice quality had a significant effect on word recognition scores. Both groups also exhibited better word recognition scores for familiar talkers. However, the familiar talker advantage in word recognition did not depend on voice quality; the familiar voice qualities of familiar talkers did not produce better word recognition scores than the unfamiliar voice qualities of familiar talkers. These combined results suggest that any integration of linguistic and indexical information in word recognition emerges from a subset of the phonetic features of the speech signal and is not, therefore, a strictly general property of speech perception.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".