Project Overview
A multimodal AI system that analyzes recorded video interviews using audio, video, and LLM-based semantic evaluation to generate objective, time-evolving candidate assessments.
System Architecture
Multimodal Temporal Analysis Pipeline (Hybrid Signal + LLM)
6-stage Analysis Pipeline
Audio/Video Feature Extraction
Whisper Speech-to-Text
AI Semantic Scorer (Qwen2.5-3B)
Temporal Performance Tracker
Interactive Analytics Dashboard
Input (AV/JD) → Parallel Extraction → Temporal Sync → Behavioral/Semantic Analysis → Output (JSON/Dashboard)
Temporal multi-stage pipeline combining signal processing and LLM-based evaluation
Semantic Evaluation & Q&A
Multimodal Analysis
Automated Q&A Pairing
Core Features
Multimodal analysis (audio + video + text)
LLM-based evaluation
Time-based scoring
Automated Q&A pairing
Behavioral metrics tracking
6-Stage Pipeline Breakdown
Stage 1
Timebase (Synchronization)
Stage 2
Extraction (Multimodal Features)
Stage 3
Temporal Segmentation
Stage 4
Behaviour Analysis
Stage 5
Semantic Scoring (LLM)
Stage 6
Aggregation (Final Verdict)
Project Impact
Reduces hiring subjectivity using data-driven evaluation
Supports scalable automated interview analysis
Tracks candidate performance evolution over time
Visual Showcase
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A multimodal AI system that analyzes recorded video interviews using audio, video, and LLM-based semantic evaluation to generate objective, time-evolving candidate assessments.
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Flow: Input (AV/JD) → Parallel Extraction → Temporal Sync → Behavioral/Semantic Analysis → Output (JSON/Dashboard)
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