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Record W2508538120

Perceptual Learning of speech sounds: A bias for 'sipper' over 'zipper'?

2016· article· en· W2508538120 on OpenAlexaffvenueabout
Molly Babel, Zoe Lawler, Carolyn Norton

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerceptionContext (archaeology)Speech recognitionSpeech perceptionSentenceStress (linguistics)PsychologyPerceptual learningLinguisticsComputer scienceCognitive psychologyNatural language processingGeography
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0430.006

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.

Opus teacher head0.063
GPT teacher head0.337
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes3
Has abstractyes

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