🔍 Project Title: AutoCon – AI-Driven Confidence Analysis for Impactful Communication

💡 Problem Statement

In a world where communication defines success—be it job interviews, public speaking, online education, or leadership—people often struggle with confidence, clarity, and emotional impact. Traditional soft-skill training methods are subjective, non-scalable, and lack real-time personalized feedback. There's a growing need for a data-driven, AI-powered solution that quantifies and improves communication confidence.

🚀 Solution Overview

AutoCon is an intelligent confidence analysis system that utilizes advanced AI to evaluate and enhance a user's communication skills. It provides deep insights into facial expressions, vocal features, and emotional tone, and delivers real-time, personalized feedback with recommendations powered by large language models.

AutoCon bridges computer vision, NLP, and audio signal processing to offer a holistic view of a user’s communication impact—all while ensuring scalability, performance, and usability.

🧠 Core Features & Workflow

1. Facial Emotion Detection

   • Uses MTCNN for accurate face detection in video streams.

   • Extracts cropped faces frame-by-frame.

   • A pre-trained CNN model built on top of the FER-2013 dataset detects micro-expressions across 7 emotions (Happy, Sad, Angry, etc.).

   • Outputs emotion timelines to gauge emotional variance and presence.

2. Speech Sentiment & Emotion Analysis

   • Extracts audio using FFmpeg.

   • Processes audio with Deepgram API to generate real-time transcripts.

   • Applies VADER sentiment analysis to the transcript for textual sentiment classification (positive, neutral, negative).

   • Correlates audio tone with spoken content to detect emotional dissonance.

3. Audio Feature Analysis with Librosa

   • Extracts pitch, energy, speech rate, and spectral features.

   • Analyzes clarity, fluency, and vocal modulation.

   • Detects filler words, stammering, or low-energy tones to give vocal feedback.

4. Posture & Gesture Recognition

   • Integration-ready with MoveNet Thunder to assess body language.

   • Posture symmetry and hand gesture energy contribute to the engagement score.

5. Insight & Recommendation Engine

   • All scores are fed into a Gemini-powered LLM that generates:

     • Personalized feedback.

     • Growth suggestions.

     • Weekly improvement plans based on emotion trends and vocal clarity.

6. Scalable Backend & User Tracking

   • Built on MongoDB Atlas to handle analysis results and user metadata at scale.

   • Tracks user progress, computes monthly averages, and offers performance dashboards.

🛠 Tech Stack

📊 Impact

• Democratizes soft-skill improvement using AI.

• Offers objective, quantifiable metrics to replace subjective feedback.

• Highly relevant for students, professionals, educators, and coaches.

• Can be deployed in corporate trainings, ed-tech, job platforms, and therapy tools.

📈 Scalability & Innovation

• Modular design with plug-and-play analysis pipelines.

• Horizontally scalable with MongoDB Atlas and async backend support.

• Real-time emotion fusion across modalities (face, voice, speech).

• Pioneers holistic, AI-driven communication scoring in a lightweight, portable desktop package.

🏆 Why AutoCon Stands Out from Others?

• Tackles a deeply human problem of public speaking with all round analysis and extensive AI insights with regard to every aspects.

• Combines CV, NLP, audio processing, and LLMs—all in one cohesive system.

• Designed with future-readiness and extensibility in mind (mobile, real-time, cloud).

• Built to not only detect—but empower users with tools to grow.