Bibliographic record
Abstract
This paper presents a new methodology for computational speaker recognition based on a mathematical model of articulatory speech production. The method, based on articulatory phonology is tested on the MOCHA database for recognizing a male speaker and a female speaker. From an engineering perspective, in articulatory phonology one is interested in the trajectories over time of a set of articulators. These time trajectories are associated with the production of speech. The basic phonological unit in articulatory phonology is the articulatory gesture, which is defined as a dynamic system specified by a characteristic set of parameters. This dynamic system receives as inputs a target state and a set of parameters that tune the system to the desired action. The output is the solution of the state equation, i.e, the state trajectory, where the state is formed by the x-y positions of the important articulators that describe human speech. The state trajectory is then mapped to the output speech waveform by emulating the human vocal tract, through the observation equation and the MFCCs frequency description. A simplification of this model will be used in this paper for speaker recognition with 100 percent success in recognizing a male and a female speaker.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".