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Record W2076481130 · doi:10.1121/1.1332376

Coarticulation: Theory, Data, and Techniques

2001· article· en· W2076481130 on OpenAlexaff
Kevin G. Munhall

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2001
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsCoarticulationComputer scienceSpeech recognition

Abstract

fetched live from OpenAlex

Acoustic phonetics is a relatively young science, having been systematized with the 1960 publication of Gunnar Fant's Acoustic Theory of Speech Production.Fant's text presented the theory buttressed by his own empirical work as well as by modeling efforts by scientists working in the United States, and the field of acoustic phonetics and its many associated disciplines ͑speech physiology, speech perception, speech synthesis, and automatic speech and speaker recognition, to name a few͒ was off and running.One of the scientists who performed some of the early, well-known modeling work was Professor Kenneth Stevens of MIT, who with Dr. Arthur House extended some of the concepts of the acoustic theory in novel ways.Now, Professor Stevens has written a text entitled Acoustic Phonetics, and it is a remarkable accomplishment.The text not only presents the stateof-the-art 40 years removed from Fant's book, but employs a probing, didactic approach that gives the material the feel of having evolved under the influence of teaching many students.This almost certainly accounts for the linear, crystal-clear way in which the information is developed and applied, and for the very creative use of figures and charts.It seems to me that after reading the ten chapters of this book, a motivated student or interested scientist with little or no previous knowledge of acoustic phonetics would know quite a lot about the subject, and in a fair amount of depth.This reader would also understand, very clearly, Professor Stevens' point of view about this material, summarized in the preface: ''The theme of this book is to explore the͑se͒ relations between the discrete linguistic features and their articulatory and acoustic manifestations'' ͑p.vii͒.Chapter 1 presents an overview of the anatomy and physiology of speech production.The anatomy is much like that presented in many textbooks dealing with the head, neck, and respiratory system, but Stevens also brings in more recent information, specifically on the fine structure of the vocal folds as well as on cross-sectional geometries of the vocal tract and sinuses derived from magnetic resonance imaging studies.The speech physiology section of the chapter is most impressive and to this writer's knowledge unique in the sense of not being available in any other source book on speech production or acoustics.This section contains extensive material on aerodynamic processes and articulatory kinematics, both critical for much of the acoustic modeling presented in subsequent chapters.Chapter 2 is a treatise on source mechanisms in speech acoustics.The accepted theory of speech acoustics is often referred to as the source-filter theory, because the output of the vocal tract is a combination of source and filter mechanisms; stated in acoustic terms, the acoustic properties of the filter shape the acoustic properties of the source.Sources can be located at lots of different locations throughout the speech mechanism, and include most notably the periodic source generated by the vibrating vocal folds and the various aperiodic sources generated at the level of the larynx and throughout the vocal tract.Stevens devotes a good deal of attention to the theory of vocal-fold vibration and its aerodynamic and acoustic consequences.This fairly complex material is developed as a series of modeling steps that always has one eye on the underlying motions of the vocal folds and the other on the aerodynamic consequences of those motions, the prize

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.011
Science and technology studies0.0010.004
Scholarly communication0.0070.007
Open science0.0040.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.010

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 head0.020
GPT teacher head0.295
Teacher spread0.276 · 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 designTheoretical or conceptual
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

Citations244
Published2001
Admission routes1
Has abstractyes

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