Speaker adaptation in infancy: The role of lexical knowledge
Bibliographic record
Abstract
The acoustic realization of words varies greatly between speakers. While adults easily adapt to speaker idiosyncrasies, infants do not possess mature signal-to-word mapping abilities. As a result, the variability in the speech signal has been claimed to impede their word recognition. This study examines whether exposure to a speaker may allow infants to better accommodate that speaker's accent. Using the Headturn Preference Procedure, 15-month-olds were presented with lists containing either familiar (e.g., ball) or unfamiliar words (e.g., bog). In experiment 1, these words were produced in infants' own accent (Canadian English); in experiment 2, they were produced in a foreign accent (Australian English). Comparable to previous work (Best etal., 2009), only infants presented with their own accent preferred to listen to familiar over unfamiliar words. Thus, without access to speaker characteristics, word recognition is limited to familiar accents. In experiment 3, the same Australian-accented stimuli were preceded by exposure to the Australian speaker. Speaker adaptation tended to correlate with the infants' vocabulary size, with greater vocabularies being indicative to more robust adaptation. We are currently testing whether vocabulary size is a mediating factor caused by general processing abilities or whether speaker adaptation is lexically driven.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".