Perceptual Learning of speech sounds: A bias for 'sipper' over 'zipper'?
Bibliographic record
Abstract
Please refer to 'supplementary file' for official abstract. Despite huge variability in the incoming acoustical information, listeners efficiently map speech sounds into appropriate categories. Perceptual learning has been proposed as a cognitive mechanism that can account for this acoustic variation by updating a listener’s existing phonetic categories using clues from the lexical context (Norris, McQueen & Cutler, 2003). Perceptual learning of speech is especially relevant in multicultural contexts, including urban centers across Canada. Listeners are exposed to accented speech and novel pronunciations of different interlocutors in their day-to-day lives, and must accommodate these productions in order to successfully communicate. While perceptual learning may assist in the processing of dialect and accent differences (Kraljic, Brennan & Samuel, 2008; Crista et al., 2012) and non-canonical speech (Kraljic, Brennan & Samuel, 2008b) across the lifespan (White & Aslin, 2011; Trude et al., 2013; Witteman et al., 2013), little is known about whether some pronunciations are easier to perceptually learn than others. The present study investigates whether a bias for perceptually learning typologically more prevalent devoiced fricatives over voiced fricatives exists by exposing listeners to sentence stimuli with sentence-final voiceless [s] and voiced [z] items (e.g. ‘He couldn't handle any more dinner, but there might be room for dessert ’). A lexical decision task follows the exposure phase, measuring participants recalibration of [z] and [s]. Stimuli were produced naturally by one speaker, without synthesizing the placement of the critical fricatives [z] or [s] (see Weatherholtz, 2015). We predict listeners will learn the typologically more common devoicing pattern better than the voicing pattern (e.g. [ ] will be learned better than [ ]). This project contributes to our understanding of which attributes of the speech signal facilitate perceptual learning.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".