Bibliographic record
Abstract
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 few 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 these relations between the discrete linguistic features and their articulatory and acoustic manifestations'' p. vii.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".