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Record W4200115547 · doi:10.1097/aud.0000000000001182

Bilinguals Show Proportionally Greater Benefit From Visual Speech Cues and Sentence Context in Their Second Compared to Their First Language

2021· article· en· W4200115547 on OpenAlexaffabout

Bibliographic record

VenueEar and Hearing · 2021
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsConcordia UniversityCentre for Research on Brain Language and Music
Fundersnot available
KeywordsSentenceSpeech perceptionContext (archaeology)PerceptionSensory cueQuality (philosophy)Context effectNoise (video)

Abstract

fetched live from OpenAlex

OBJECTIVES: Speech perception in noise is challenging, but evidence suggests that it may be facilitated by visual speech cues (e.g., lip movements) and supportive sentence context in native speakers. Comparatively few studies have investigated speech perception in noise in bilinguals, and little is known about the impact of visual speech cues and supportive sentence context in a first language compared to a second language within the same individual. The current study addresses this gap by directly investigating the extent to which bilinguals benefit from visual speech cues and supportive sentence context under similarly noisy conditions in their first and second language. DESIGN: Thirty young adult English-French/French-English bilinguals were recruited from the undergraduate psychology program at Concordia University and from the Montreal community. They completed a speech perception in noise task during which they were presented with video-recorded sentences and instructed to repeat the last word of each sentence out loud. Sentences were presented in three different modalities: visual-only, auditory-only, and audiovisual. Additionally, sentences had one of two levels of context: moderate (e.g., "In the woods, the hiker saw a bear.") and low (e.g., "I had not thought about that bear."). Each participant completed this task in both their first and second language; crucially, the level of background noise was calibrated individually for each participant and was the same throughout the first language and second language (L2) portions of the experimental task. RESULTS: Overall, speech perception in noise was more accurate in bilinguals' first language compared to the second. However, participants benefited from visual speech cues and supportive sentence context to a proportionally greater extent in their second language compared to their first. At the individual level, performance during the speech perception in noise task was related to aspects of bilinguals' experience in their second language (i.e., age of acquisition, relative balance between the first and the second language). CONCLUSIONS: Bilinguals benefit from visual speech cues and sentence context in their second language during speech in noise and do so to a greater extent than in their first language given the same level of background noise. Together, this indicates that L2 speech perception can be conceptualized within an inverse effectiveness hypothesis framework with a complex interplay of sensory factors (i.e., the quality of the auditory speech signal and visual speech cues) and linguistic factors (i.e., presence or absence of supportive context and L2 experience of the listener).

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)0.001
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.061
GPT teacher head0.339
Teacher spread0.278 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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