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Record W1970193572 · doi:10.1121/1.4786950

Auditory, but perhaps not visual, processing of Lombard speech

2006· article· en· W1970193572 on OpenAlexaff
Eric Vatikiotis‐Bateson, V.W.W. Chung, Kevin Lutz, Nicole Mirante, Jolien Otten, Johanna Tan

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsQUIETSurpriseSpeech perceptionMasking (illustration)Noise (video)PerceptionAudiologySpeech recognitionPsychologyAcousticsComputer scienceCommunicationPhysicsMedicineArtificial intelligenceArt

Abstract

fetched live from OpenAlex

Perception results for three studies are presented that address the role of Lombard speech in auditory, visual, and auditory-visual speech perception. Predictably, when presented in auditory-only conditions with masking noise, listeners recover speech recorded in noise (Lombard speech) better than speech recorded in quiet and presented with the same level of masking noise. However, there is almost no difference in listener performance when Lombard and quiet speech are presented audiovisually with masking noise. Both conditions are enhanced compared to auditory-alone conditions, but there is no indication that the facial motion correlates, demonstrated previously for quiet speech [H.C. Yehia, et al., Speech Commun. 26, 23–44 (1998)], play as strong a role in enhancing auditory-visual processing of Lombard speech, even though Lombard speech is accompanied by larger facial motions. Perhaps it is no surprise that, at a cocktail party, one leans in with an ear rather than with the eyes. [Research supported by CFI and NSERC.]

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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.

Study designBench or experimental
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

Citations6
Published2006
Admission routes1
Has abstractyes

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