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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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".