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Cross-Modal Effects in Speech Perception

2018· article· en· W2885292263 on OpenAlexaff
Megan Keough, Donald Derrick, Bryan Gick

Bibliographic record

VenueAnnual Review of Linguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsModalitiesCongruence (geometry)ModalPerceptionStimulus modalityPsychologySensationSpeech perceptionFocus (optics)Cognitive psychologySpeech recognitionComputer scienceCommunicationSocial psychologySociologyNeuroscience

Abstract

fetched live from OpenAlex

Speech research during recent years has moved progressively away from its traditional focus on audition toward a more multisensory approach. In addition to audition and vision, many somatosenses including proprioception, pressure, vibration and aerotactile sensation are all highly relevant modalities for experiencing and/or conveying speech. In this article, we review both long-standing cross-modal effects stemming from decades of audiovisual speech research as well as new findings related to somatosensory effects. Cross-modal effects in speech perception to date are found to be constrained by temporal congruence and signal relevance, but appear to be unconstrained by spatial congruence. Far from taking place in a one-, two- or even three-dimensional space, the literature reveals that speech occupies a highly multidimensional sensory space. We argue that future research in cross-modal effects should expand to consider each of these modalities both separately and in combination with other modalities in speech.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.032
GPT teacher head0.432
Teacher spread0.400 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2018
Admission routes1
Has abstractyes

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