Speech resynthesis from 1/f<b> <i>β</i> </b> noise
Bibliographic record
Abstract
Speech can be resynthesized from acoustic parameters estimated from the motion of the vocal tract articulators, head, and face. This has shown that speech information is broadly distributed both in space and time across the physiological systems associated with speech production, and correspondingly, it provides a basis for exploring what properties of signal structure are relevant for specifying the relation between acoustics and physiology. In this presentation, we use the resynthesis paradigm to explore the extent to which the acoustic-articulatory relation can be specified in terms of the structure of fluctuations in the speech signal. Specifically, we demonstrate that speech acoustics closely matching the original can be obtained by replacing the vocal tract kinematic signals typically used in the re-synthesis paradigm with surrogate signals having 1/fβfractal fluctuation structure—a known (Voss & Clarke 1975) statistical property of the speech amplitude envelope that holds for low frequencies corresponding to the rate of articulatory motion (<10 Hz: Dudley, 1939). We also address the hypothesis that quantitative descriptors derived from both the fractal and multifractal properties of speech link the time-varying properties of speech acoustics to the cooperative motion of physiological structures involved in speech production.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.004 | 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".