Human Behavioural Threat Analysis

Human Behavioral Threat Analyzer The Human Behavioral Threat Analyzer is a multimodal AI system designed to detect potential threat, distress, or emergency indicators from human communication by analyzing both speech audio and spoken language content. Project Overview The system combines audio-based emotion recognition with natural language understanding to assess a person's behavioral state and estimate potential risk levels. Instead of relying solely on what a person says, it also considers how they say it, making the analysis more robust and realistic. Core Components Speech-to-Text Module Uses OpenAI Whisper to transcribe spoken audio into text. Audio Behavior Analysis Uses WavLM to extract emotional and acoustic features from speech. Trained and evaluated using datasets such as: CREMA-D RAVDESS Detects emotional indicators such as stress, anger, fear, and distress. Text Risk Analysis Uses DeBERTa-v3 to analyze the transcribed text for linguistic signs of risk, coercion, panic, or suspicious intent. Multimodal Fusion Layer Combines audio and text features using a cross-attention or feature-fusion mechanism. Produces a unified behavioral assessment rather than separate predictions. Expected Output Classes The model classifies individuals into behavioral risk categories such as: Normal Stressed Distressed Coercion Detected Hidden Emergency Technologies Used Python PyTorch Hugging Face Transformers Whisper WavLM DeBERTa-v3 Audio Processing Libraries NLP & Deep Learning Your Role You acted as the AI/ML Engineer and Project Lead, responsible for: Designing the overall multimodal architecture. Researching state-of-the-art speech and language models. Building the audio emotion recognition pipeline using WavLM. Integrating speech transcription and NLP components. Developing the multimodal fusion strategy. Training, evaluating, and optimizing deep learning models. Conducting dataset preparation and preprocessing. Defining threat/risk classification logic and system workflow.