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Record W2144089558

Effects of training modality on audio-visual perception of nonnaitve speech contrasts

2008· article· en· W2144089558 on OpenAlexafffundvenueabout
Yue Wang, Dawn Behane, Haisheng Jiang

Bibliographic record

VenueCanadian acoustics · 2008
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHeadsetAudiologyMandarin ChineseModality (human–computer interaction)PerceptionActive listeningPsychologySpeech perceptionSpeech recognitionArticulation (sociology)Computer scienceCommunicationMedicineLinguisticsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The effects of training modality on audio-visual perception of nonnative speech contrasts was investigated. Forty-four young adult mandarin Chinese natives with less than five years' residency in Canada participated in the study. Pre/posttest stimuli were recorded of an adult male speaker of Canadian English. The stimuli were based on 18 English CV syllables having a fricative followed by a vowel. The fricatives differed in place of articulation and the participants task was to identify the fricatives while listening to the sounds over a headset, or viewing the speaker mouth movements on the screen, or both. the perceptual training program followed the high variability procedure demonstrated to be highly effective in auditory perception training. The results revealed a noticeable effect of training modality, where the extent of post-training improvement was consistent with the type of training.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.039
GPT teacher head0.324
Teacher spread0.285 · 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
Published2008
Admission routes4
Has abstractyes

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