Bibliographic record
Abstract
Voice is often described as an “auditory face”; it provides important information concerning speaker identity (e.g., age, height, sex). The acoustic properties related to voice can also vary substantially within a speaker based on one’s emotional, social, and linguistic states. Recent work suggests that biological components have the greatest impact in the acoustic variability found in voice, followed by language-specific factors and speaking style [Lee & Kreiman, J. Acoust. Soc. Am. 153, A295 (2023)]. The effects of such within- vs. between-speaker acoustic variability on the perception of speaker identity, however, have not been explored. The present study therefore examines the perception of speaker identity in bilingual voices. The prediction is that acoustic variability will also affect speaker identity perception: voices will be discriminated best for between-speaker samples, while within-speaker variability will not affect perception of speaker to the same extent. To test this prediction, listeners participated in a voice discrimination task using bilingual voice data produced by Korean heritage speakers across different languages (Korean, English) and speech styles (read, extemporaneous). The data will be analyzed to measure the effects of speaker, language, and speech style on voice discrimination. The results will be reported in relevance to the relationship between bilingualism and speech style on voice quality and speaker identity.
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 | 0.001 |
| 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".