Introducing phonetics students to spectral components of vowel-like sounds
Bibliographic record
Abstract
Undergraduate students in phonetics classes typically have difficulty understanding the concept behind Fourier analysis: that vowels can be decomposed into a series of spectral components. The concept can be introduced to students beginning with its inverse: that vowel-like sounds can be constructed from a series of sine waves. The demonstration consists of a Praat script which students are given to work with as a homework assignment before vowels are covered in class (Praat is free cross-platform software). The students are asked to input three frequencies, the script plots the time-domain representation of each sine wave and the sum of the three sine waves, and the frequency-domain representation of the sum of the three sine waves. It also plays the three sine waves and the sum of the three sine waves. Instructions include suggestions of frequencies for the students to try, and ask which vowels the results sound most like. Instructions also ask students to experiment with different frequencies to try to make sounds similar to other vowels. The students gain hands-on experience with vowel-like synthesis in order to give them an intuitive sense of the spectral components of vowel-like sounds before the theoretical concepts are introduced in the classroom.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".