Bibliographic record
Abstract
This study investigates perceptual voice identification by analyzing the quantitative and qualitative acoustical characteristics listeners use to identify voices in non-native speech. Research on voice identification typically focuses on the direct quantitative comparison of voices to find unique identifiers [e.g., Morrison (2009) and Jessen (2008)]. However, in this study an experiment was run in order to analyze the acoustic cues people perceptually adhere to in identifying voices. Listeners of various native language backgrounds were asked to identify the voices of Mandarin Chinese speakers in both Mandarin and Mandarin-accented English speech. Listeners’ accuracy and speaker selections in fourteen voice line-ups (Sullivan and Schlichting 1998) were recorded. Speakers’ productions for all stimuli were analyzed using twelve acoustic features. The results find that though listeners were highly accurate at the voice recognition task, their errors do follow systematic trends. Quantitative and qualitative acoustic measures such as pitch, pitch variation, formant trajectories, intensity, and nasality prove to be reliable in patterning memorable and forgettable voices. That is, this study finds that certain acoustic characteristics are more salient in everyday voice identification.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".