Towards real-time two-dimensional wave propagation for articulatory speech synthesis
Bibliographic record
Abstract
The precise simulation of voice production is a challenging task, often characterized by a tradeoff between quality and speed. The usage of 3D acoustic models of realistic vocal tracts produces extremely precise results, at the cost of running simulations that may take several minutes to synthesize a few milliseconds of audio. In contrast, 1D articulatory vocal synthesizers rely on highly simplified acoustic and anatomical models to achieve real-time performances, but can only partially match the spectra of realistic vocal tracts. In this work, we present a novel articulatory vocal synthesizer, based on a fast 2D propagation model running on a graphics card (GPU). The system can run in real-time under specific conditions and, differently from 1D synthesizers, allows for simulating airflow propagation through asymmetric and curved geometries. This paper covers details on the GPU implementation of the different components of the system, including the 2D Finite-Difference Time-Domain wave solver and the excitation mechanism. A preliminary evaluation is presented, using area functions to simulate static vowels. Three different resolutions are tested, combined with two alternative ways of discretizing the 2D geometries. The computed formants are overall characterized by small positional errors while computational times are comparable with those from 1D systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".