Deception Analysis Using Deep Learning Based on Voice Stress Detection
Bibliographic record
Abstract
Voice Stress Detection concurrently is a sorcery that targets to deduce deception calculated by identifying the amount of stress in the voice signal. It becomes conceivable to detect the stressed voice in this century with the significant development, Artificial Intelligence (AI). Voice, being the core for communication is a good source of input signal to an AI model to analyze deception. The demand for healthy mental life of this era is the prime objective tried to be fulfilled with this work. The difference in the fluency of speech of a stressed person from that of an unstressed using the Deep Learning method of Convolutional Neural Network (CNN) is the featured sweep of this work. The dataset used for the implementation of the CNN model for analysing deception is The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). RAVDESS is a combination of unisexual voices of 24 different subjects with six emotions, which was analysed using the neural network and later binary classified into stressed or unstressed. The CNN model is implemented at the beginning on the voice of a single actor followed by 24 actors. A comparison on the existing Machine Learning models with Deep Learning model was also performed. An accuracy of 72.5% was obtained in classifying voice with an acceptable percentage of true positives with the CNN.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".