Language Specificity in Phonetic Cue Weighting: Monolingual and Bilingual Perception of the Stop Voicing Contrast in English and Spanish
Bibliographic record
Abstract
BACKGROUND/AIMS: This work examines the perception of the stop voicing contrast in Spanish and English along four acoustic dimensions, comparing monolingual and bilingual listeners. Our primary goals are to test the extent to which cue-weighting strategies are language-specific in monolinguals, and whether this language specificity extends to bilingual listeners. METHODS: Participants categorized sounds varying in voice onset time (VOT, the primary cue to the contrast) and three secondary cues: fundamental frequency at vowel onset, first formant (F1) onset frequency, and stop closure duration. Listeners heard acoustically identical target stimuli, within language-specific carrier phrases, in English and Spanish modes. RESULTS: While all listener groups used all cues, monolingual English listeners relied more on F1, and less on closure duration, than monolingual Spanish listeners, indicating language specificity in cue use. Early bilingual listeners used the three secondary cues similarly in English and Spanish, despite showing language-specific VOT boundaries. CONCLUSION: While our findings reinforce previous work demonstrating language-specific phonetic representations in bilinguals in terms of VOT boundary, they suggest that this specificity may not extend straightforwardly to cue-weighting strategies.
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 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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".