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Record W3214732687

Examining the Effect of Mask Use on Speech and Face Perception

2021· article· en· W3214732687 on OpenAlexaff
Sam Smith-Ackerl

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPsychologyPerceptionProsodyFacial expressionFace (sociological concept)Cognitive psychologyForeheadAffect (linguistics)CommunicationSpeech recognitionComputer scienceLinguisticsMedicineNeuroscience
DOInot available

Abstract

fetched live from OpenAlex

Facial perception has become increasingly challenging due to the Covid-19 pandemic. Research has demonstrated that facial expressions are one of the most important factors when examining human communication. However, the use of masks has obstructed one of the most communicative aspects of human beings. Research on speech perception has demonstrated that most individuals rely on the use of the eyes and mouth when using the face for communicative purposes. Facial movement and expression also provide visual stimuli to assist individuals when interpreting speech, however these aspects are most utilized when the mouth is not obstructed. Research has demonstrated that preventing visualization of the bottom half the face forces humans to rely on the visual structures that are not masked, such as the eyes, and forehead. The continuous use of masks in our everyday life may have increased our ability to interpret speech without being able to perceive the bottom half of faces. In other words, having to communicate on a regular basis with people wearing masks, might have forced us to be more sensitive to the eyes and prosody of the face and head. Our study aims to examine how masks affect speech and facial perception due to the constant use of masks. The presented study will highlight whether or not individuals have improved their ability to understand verbal stimuli presented from a masked face. Department: Psychology Faculty Mentor: Dr. Michelle Jarick

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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)0.0000.000

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 head0.215
GPT teacher head0.400
Teacher spread0.186 · 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 teacher head, 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
Published2021
Admission routes1
Has abstractyes

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