Day 1: Laying the Groundwork for the AI Lie Detector App #voiceai #llmtools

SEO Title: Day 1: Laying the Groundwork for the AI Lie Detector App #voiceai #llmtools
Focus Keyphrase: AI lie detector
Meta Description: Start building an AI lie detector app using voice emotion and text semantics. In this intro, we’ll set up the project, explain how lie detection works, and install base tools.

Table of Contents

What Is an AI Lie Detector?

An AI lie detector app analyzes both how you say something (emotion) and what you say (logic) to estimate truthfulness. By combining voice emotion analysis and language-based reasoning, we can simulate a polygraph-style system — but 100% digital and AI-powered.

Tech Stack We’ll Use

Frontend: HTML + JS with Web Audio API
Backend: Python (Flask or FastAPI)
AI: DeepSpeech or Whisper for transcription, PyAudio for voice
Emotion: py-webrtcvad + HEAR models
Semantics: OpenAI or Claude (to analyze statement contradictions)

Why This Project Is Special

This isn’t just voice transcription. Our AI lie detector uses:

Voice vibration and tremble analysis (possible indicators of stress)
Contradiction detection via GPT (e.g., “I’m happy” vs voice says otherwise)
Real-time browser-based microphone capture

Step 1: Project Setup

Create a new folder for the app:

mkdir ai-lie-detector
cd ai-lie-detector

Then create:


├── backend/
│   ├── app.py
│   └── emotion_analyzer.py
├── frontend/
│   └── index.html
└── requirements.txt

Step 2: Install Base Python Tools

pip install openai whisper faster-whisper flask webrtcvad soundfile

Also install FFmpeg globally, which is required by most audio packages.

Coming Up Tomorrow

In Day 2: Capture Microphone Input in the Browser, we’ll create the frontend with Web Audio API and send voice to the backend for transcription.

Tags: #AIUX #LieDetection #VoiceAI #OpenAI

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