Project Overview

This project aims to develop a non-intrusive AI system that can analyze voice recordings or real-time speech to detect and estimate an individual's stress level. By processing vocal cues such as pitch, tone, tempo, and intensity, the system will infer emotional and physiological stress indicators using advanced machine learning models.

Human voice carries subtle acoustic variations that often reflect psychological states. This project seeks to capture these variations and translate them into quantifiable stress scores. The solution can be integrated into telehealth applications, call centers, workplace wellness programs, or wearable devices for continuous stress monitoring.

Objectives:

• Build a voice-based stress detection system using AI and signal processing techniques.
• Analyze acoustic parameters to assess stress levels in real-time or recorded speech.
• Generate stress level indicators on a continuous or categorical scale (e.g., Low, Medium, High).

Expected Outcome

• A functional prototype capable of detecting stress levels from voice with reliable accuracy.
• Real-time feedback system or dashboard that displays stress level indicators.
• Dataset curated for voice-based stress analysis research.
• Integration-ready API.

How to Apply

We invite researchers, developers, and AI enthusiasts to submit proposals for solving this open problem. Each proposal should include:

• Literature Review: Summary of existing research in speech emotion recognition and stress detection.
• Proposed Approach: Detailed explanation of feature extraction, model architecture, and classification techniques.
• Dataset Plan: Description of the data sources, collection strategy, and preprocessing techniques.
• Implementation Plan: Step-by-step process including model training, validation, and deployment.
• Innovation: Highlight novel methods such as multimodal analysis (voice + facial expression), self-supervised pretraining, or adaptive models for diverse users.
• Timeline and Deliverables: Clear project milestones and expected outcomes.