The role of passage length on acoustic voice variability in bilingual speech
Bibliographic record
Abstract
An individual's voice is determined in part by the limitations of their anatomy and physiology, in addition to language-specific phonological and phonetic structure. When a bilingual switches between languages, how much do they change their voice? Previous work using a corpus of spontaneous speech from early Cantonese-English bilinguals found surprisingly little variability across individuals' languages [Johnson et al., Proc. of Interspeech (2020)] compared to earlier research on across-talker acoustic voice variability [Lee et al., JASA (2019)]. A crucial difference between these two studies, however, is passage length. A longer passage (e.g., 30 min) potentially allows for a more stable structure to emerge in a principal components analysis, while a shorter sample (e.g., 2 min or less) may instead be subject to ephemeral variation, and potentially misrepresent the overall variability of a voice. Building on Johnson et al. (2020), the present study asks: to what extent does passage length impact the results of principal components and canonical redundancy analyses designed to elucidate within-talker (across languages) and across-talker (within language) idiosyncratic variation? These results are important for theories of talker recognition, identification, and discrimination, in addition to improving understanding of talker-specific acoustic-phonetic variation.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".