Evaluating Speech Production-based Acoustic Features for COVID-19 Classification using Cough Signals
Bibliographic record
Abstract
Cough is a prevalent acoustic event that contains rich information about underlying ailment in a person. It is used in the diagnosis of several respiratory illnesses including Asthma, Pneumonia, and Tuberculosis. According to medical surveys, cough has been ascertained as a major symptom of the recently declared pandemic, the novel Coronavirus disease (COVID-19). In this work, we attempt to classify COVID-19 positive and negative subjects based on their respective cough recordings. Towards this end, the effectiveness of certain acoustic parameters related to the glottal source and vocal tract of the speech production system, along with spectro-temporal information of the cough signal has been studied for classifying COVID-19 positive and negative samples. These parameters are later used for training a multi-layer-perceptron classifier. The training and performance assessment of this system is done using cough data samples provided in DiCOVA 2021 challenge. Results obtained show that the proposed system outperforms the baseline system, in classifying COVID-19 subjects.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".