Development of AI Stress/Aggression Detection in Customer Voice
Acoustic detection of stress and aggression works without analyzing words — only by voice characteristics: speech rate, pitch frequency (F0), energy, tremor. Allows responding in 2–3 seconds before the person utters a threat.
Acoustic Markers of Stress and Aggression
Features extracted:
- F0 mean and range (aggression: increase >20%)
- Speaking rate (stress: acceleration or slowdown)
- Energy mean (aggression: significant increase)
- Jitter, shimmer, HNR ratio (stress indicators)
ML Classifier
Use Gradient Boosting classifier on acoustic features. Accuracy: ~78–85% on 3 classes (neutral / stressed / aggressive).
Integration into Call Stream
Continuous emotion monitoring with 3-second analysis windows. Baseline established from first 10 seconds. Alert triggered for aggressive detection with >0.75 confidence.
Timeline: classifier on ready dataset — 2–3 weeks. Dataset collection and training from scratch — 2–3 months.