Acoustic consistency of Mandarin–English bilingual voices: Evidence from the Mandarin–English Language Interview (MELI) Corpus
Bibliographic record
Abstract
This study examines how consistently bilingual talkers maintain their voice quality across languages by comparing acoustic properties of bilinguals’ Mandarin and English speech. Using the MELI (Mandarin–English Language Interview) Corpus, we analyzed read and interview speech from 51 Mandarin–English bilinguals (26 self-identified females and 25 self-identified males), with each contributing 20–30 min of high-quality recordings in both languages. Building on previous analyses (Lee et al., 2019, JASA; Johnson & Babel 2023, JASA), we extracted 24 source- and filter-based acoustic measures to characterize talker voice. Canonical Redundancy Analysis was used to quantify how well principal components of a talker’s voice in one language could be predicted by canonical varieties from the other. Results show that bilingual talkers exhibit highly consistent voice characteristics across Mandarin and English, with a mean cross-language within-talker redundancy index of 0.92—higher than both within-language cross-talker (M = 0.87) and cross-language cross-talker (M = 0.86) values. A Welch’s t-test confirmed that within-talker indices were significantly higher than cross-talker indices (t(103.93) = −14.27, p < 0.001). These findings provide new evidence that individual voice quality remains stable across languages, highlighting the structured nature of acoustic voice variation in bilingual speech.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".