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Record W2122994642 · doi:10.1109/icassp.2001.941035

A functional articulatory dynamic model for speech production

2002· article· en· W2122994642 on OpenAlex
L.J. Lee, Paul Fieguth, Li Deng

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCoarticulationSpeech productionComputer scienceSpeech recognitionNonlinear systemVocal tractFeature (linguistics)Speech synthesisHidden Markov modelReduction (mathematics)Artificial intelligenceMathematicsVowel

Abstract

fetched live from OpenAlex

Introduces a statistical speech production model. The model synthesizes natural speech by modeling some key dynamic properties of vocal articulators in a linear/nonlinear state-space framework. The goal-oriented movements of the articulators (tongue tip, tongue dorsum, upper lip, lower lip, and jaw) are described in a linear dynamic state equation. The resulting articulatory trajectories, combined with the effects of the velum and larynx, are nonlinearly mapped into the acoustic feature space (MFCCs). The key challenges in this model are the development of a nonlinear parameter estimation methodology, and the incorporation of appropriate prior assumptions to assert in the articulatory dynamic structure. Such a model can also be directly applied to speech recognition to better account for coarticulation and phonetic reduction phenomena with considerably fewer parameters than HMM based approaches.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.230

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 studies0.0000.000
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 head0.037
GPT teacher head0.235
Teacher spread0.198 · 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

Quick stats

Citations23
Published2002
Admission routes2
Has abstractyes

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