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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".