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Record W4205896026 · doi:10.1017/cbo9780511843891

Audiovisual Speech Processing

2012· book· en· W4205896026 on OpenAlex
Gérard Bailly

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2012
Typebook
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsComputer scienceVocal tractSpeech recognitionPerceptionSpeech perceptionSpeech synthesisSpeech processingMotor theory of speech perceptionModalitiesMental lexiconSpeech productionModality (human–computer interaction)Human–computer interactionPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

When we speak, we configure the vocal tract which shapes the visible motions of the face and the patterning of the audible speech acoustics. Similarly, we use these visible and audible behaviors to perceive speech. This book showcases a broad range of research investigating how these two types of signals are used in spoken communication, how they interact, and how they can be used to enhance the realistic synthesis and recognition of audible and visible speech. The volume begins by addressing two important questions about human audiovisual performance: how auditory and visual signals combine to access the mental lexicon and where in the brain this and related processes take place. It then turns to the production and perception of multimodal speech and how structures are coordinated within and across the two modalities. Finally, the book presents overviews and recent developments in machine-based speech recognition and synthesis of AV 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.524
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.217
Teacher spread0.195 · 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