Robust Assessment of Dysarthrophonic Voice with RASTA-PLP Features: A Nonlinear Spectral Measures
Bibliographic record
Abstract
This paper presents an artificial intelligence based speech signal processing technique to identify dysarthrophonic voice with relative spectral-perceptual linear prediction (RASTA-PLP) features. Dysarthria is a neural motor speech disorder caused by muscular weakness. Voice analysis of dysarthrophonic patients is challenging as this disease has multidimensional effects on the human voice generation system. Conventional spectral analysis is unable to accurately characterize the pathology associated with nonlinear dynamicity of human voice. This work investigates the suitability of RASTA-PLP features excerpted from speech signals to identify dysarthrophonic patients. The speech samples of healthy and dysarthrophonic patients are collected from the Saarbrücken Voice Database (SVD). Several machine learning and Artificial neural network (ANN) based algorithms are developed to evaluate the classification performance of the proposed system. The designed system can achieve excellent performance in terms of accuracy (100%) considering female and male subjects separately.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".