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Visual Prosody and Speech Intelligibility

2004· article· en· W2118004237 on OpenAlexaff
Kevin G. Munhall, Jeffery A. Jones, Daniel E. Callan, Takaaki Kuratate, Eric Vatikiotis‐Bateson

Bibliographic record

VenuePsychological Science · 2004
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British ColumbiaQueen's University
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsProsodyIntelligibility (philosophy)PsychologyAnimationPerceptionGestureHead (geology)Motion (physics)Speech perceptionRhythmCommunicationActive listeningNonverbal communicationSpeech recognitionCognitive psychologyAcousticsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

People naturally move their heads when they speak, and our study shows that this rhythmic head motion conveys linguistic information. Three-dimensional head and face motion and the acoustics of a talker producing Japanese sentences were recorded and analyzed. The head movement correlated strongly with the pitch (fundamental frequency) and amplitude of the talker's voice. In a perception study, Japanese subjects viewed realistic talking-head animations based on these movement recordings in a speech-in-noise task. The animations allowed the head motion to be manipulated without changing other characteristics of the visual or acoustic speech. Subjects correctly identified more syllables when natural head motion was present in the animation than when it was eliminated or distorted. These results suggest that nonverbal gestures such as head movements play a more direct role in the perception of speech than previously known.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.084
GPT teacher head0.479
Teacher spread0.395 · 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

Citations482
Published2004
Admission routes1
Has abstractyes

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