🎯 AI-Powered Multimodal Interview Intelligence System

A production-ready AI system that analyzes recorded interview videos using speech, text, and facial cues to generate objective, explainable interview evaluation reports.

This project focuses on real-world usability, clean architecture, and reproducibility, not just model accuracy.

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🚀 Why This Project Exists

Interview evaluation is often:

• subjective
• inconsistent
• biased
• hard to scale

This system provides a structured, data-driven alternative by analyzing:

• how a candidate speaks
• what the candidate says
• how engaged the candidate appears

The result is a clear interview intelligence report that recruiters can actually use.

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🧠 What the System Does

1. Accepts a recorded interview video
2. Extracts audio and video frames
3. Analyzes:
   • 🎤 Speech (confidence, pace, fillers)
   • 📝 Answer quality (relevance, clarity)
   • 👁️ Facial engagement (eye contact, stability)
4. Combines all signals into a final interview score
5. Generates human-readable feedback

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🏗️ High-Level Architecture

Interview Video
     ↓
Video Processor (Audio + Frames)
     ↓
Speech Analysis  ←→  NLP Evaluation  ←→  Facial Analysis
     ↓
Hybrid Scoring Engine
     ↓
Interview Intelligence Report

Each module is independent, explainable, and testable.

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🧪 Key Features

• Multimodal AI (Audio + NLP + Vision)
• OpenAI Whisper for speech-to-text
• Transformer embeddings for semantic analysis
• MediaPipe Face Mesh for engagement analysis
• Hybrid rule + ML scoring
• Clean Streamlit UI for recruiters
• Modular, production-style codebase

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🛠️ Tech Stack

Backend / AI

• Python 3.10
• PyTorch
• Hugging Face Transformers
• OpenAI Whisper
• Librosa
• MediaPipe
• OpenCV
• NumPy / SciPy
• FFmpeg

Frontend

• Streamlit

Dev & Quality

• Git & GitHub
• Pytest
• Black
• Flake8

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🐍 Python Version (IMPORTANT)

This project requires Python 3.10.x.

Newer Python versions (3.11+) may cause incompatibilities with
PyTorch, MediaPipe, Librosa, and Whisper.

Verified working version

• Python 3.10.11

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⚙️ Installation & Setup

Prerequisites

• Python 3.10.x
• FFmpeg installed and added to PATH

Setup

git clone https://github.com/your-username/AI-Interview-Intelligence.git
cd AI-Interview-Intelligence

py -3.10 -m venv venv
venv\Scripts\activate

pip install -r requirements.txt
streamlit run app.py

Then open:

http://localhost:8501

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▶️ How to Use

1. Launch the Streamlit app
2. Upload an interview video (MP4 / MOV / AVI)
3. Enter the interview question
4. (Optional) Add expected keywords
5. Click Analyze Interview
6. View scores, breakdowns, and feedback

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📊 Output You Get

• Final Interview Score (A/B/C style grading)
• Speech metrics (WPM, filler ratio, confidence)
• NLP scores (relevance, clarity)
• Facial engagement score
• Strengths & improvement areas
• Hiring-style recommendation

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⚠️ Limitations

• Designed for single-speaker interviews
• Requires reasonable lighting & audio
• Batch processing (not real-time yet)
• Optimized for interviews up to ~10 minutes

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🔮 Future Improvements

• Real-time interview analysis
• FastAPI backend
• ATS (Applicant Tracking System) integration
• Emotion recognition (voice + face)
• Multi-speaker support
• Cloud deployment

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🧑‍💻 Why This Is Different from Typical ML Projects

• Not a notebook-only demo
• Modular, production-style architecture
• Explainable scoring (not black-box)
• Actually usable by non-technical users
• Built with deployment and reproducibility in mind

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📄 License

MIT License — free to use, modify, and extend.

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🎤 Interview-Ready Summary

“I built a multimodal AI system that evaluates interview performance by combining speech analysis, NLP-based answer evaluation, and facial engagement analysis, producing explainable hiring intelligence rather than just raw predictions.”