Transforming Forensic Psychology and Mental Health with Neural Network-Based Emotion Recognition
Bibliographic record
Abstract
In today's world, emotion recognition technology has emerged as a vital tool in mental health assessment and forensic psychological analysis which provides a more data driven evaluation process compared to the state of art methods.The proposed work provides an artificial neural network (ANNs) for voice-based emotion detection in clinical and legal domains which focused on the implications for forensic psychology and witness credibility assessment in legal investigations.The proposed methodology considers the speech features such as vocal range, pitch and tone which is used for the detection of stress, trauma, and potential mental health concerns.The proposed work is evaluated on three standard datasets: RAVDESS, TIMIT and EMO-DB database.As per the analysis, it is observed that ANN based methodology demonstrated significant improvement in accuracy and the precision is increased to 80.21%, and 84.11% and 86.2% are obtained respectively.The proposed work reduced the practical subjective bias credibility evaluation up to 40% highlighting the potential of emotion recognition systems to enhance diagnostic precision in psychological testing and improve fairness in forensic and legal proceedings.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".