Top-down influence on phonetic categorization of native vs. non-native speech
Bibliographic record
Abstract
Speech perception requires integration of multiple sources of information, including bottom-up acoustic information and top-down contextual information, and listeners may adjust their reliance on a given source of information depending on the communicative context. This work tests the hypothesis that listeners increase reliance on contextual, relative to acoustic, information when listening to a talker with a foreign accent, under the assumption that the bottom-up information (non-native pronunciation) may be less reliable. Native English listeners categorized an utterance-final target word, where the initial consonant systematically varied in voice onset time (VOT), as either “goat” or “coat.” Target words were embedded in carrier sentences contextually biased towards one of the words (e.g., “The girl milked the [coat/goat]” vs. “The girl put on her [coat/goat]”). Stimuli were created from productions by two talkers: a native English talker and a native Mandarin/L2 English talker with a discernable foreign accent. As expected, acoustic information (VOT) was the primary cue for categorization, but sentence context also influenced perception in both talker conditions. Furthermore, preliminary results indicate that the semantic context effect is larger in the Accented than in the Native condition, suggesting that listeners do indeed increase reliance on contextual information when listening to foreign-accented speech.
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 | 0.003 |
| 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 | 0.001 |
| 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".