Detecting non-modal phonation in telephone speech
Bibliographic record
Abstract
Non-modal phonation conveys both linguistic and paralinguistic information, and is distinguished by acoustic source and filter features. Detecting non-modal phonation in speech requires reliable F0 analysis, a problem for telephone-band speech, where F0 analysis frequently fails. We demonstrate an approach to the detection of creaky phonation in telephone speech based on robust F0 and spectral analysis. Our F0 analysis relies on an autocorrelation algorithm applied to the intensity-boosted and inverse-filtered speech signal and succeeds in regions of nonmodal phonation where the non-filtered F0 analysis typically fails. In addition to the extracted F0 values, spectral amplitude is measured at the first two harmonics (H1, H2) and the first three formants (A1, A2, A3). Visual and spectral inspection of the detected creaky phonation confirms the findings reported from laboratory setting. Statistical analysis using oneway ANOVA and classification using Support Vector Machine (SVM) reveals promising results which lead to further improvement for automatic detection of non-modal phonation in telephone speech. 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".