Bibliographic record
Abstract
We present here a musical approach to speech melody, one that takes advantage of the intervallic precision made possible with musical notation. Current phonetic and phonological approaches to speech melody either assign localized pitch targets that impoverish the acoustic details of the pitch contours and/or merely highlight a few salient points of pitch change, ignoring all the rest of the syllables. We present here an alternative model using musical notation, which has the advantage of representing the pitch of all syllables in a sentence as well as permitting a specification of the intervallic excursions among syllables and the potential for group averaging of pitch use across speakers. We tested the validity of this approach by recording native speakers of Canadian English reading unfamiliar test items aloud, spanning from single words to full sentences containing multiple intonational phrases. The fundamental-frequency trajectories of the recorded items were converted from hertz into semitones, averaged across speakers, and transcribed into musical scores of relative pitch. Doing so allowed us to quantify local and global pitch-changes associated with declarative, imperative, and interrogative sentences, and to explore the melodic dynamics of these sentence types. Our basic observation is that speech is atonal. The use of a musical score ultimately has the potential to combine speech rhythm and melody into a unified representation of speech prosody, an important analytical feature that is not found in any current linguistic approach to prosody.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".