VOICE STRESS DETECTION SYSTEM

Abstract

The Voice Stress Detection System (VSDS) is an intelligent and efficient solution designed to identify stress levels in human speech by analyzing acoustic and speech-based features. Human emotions such as stress, anxiety, and nervousness significantly influence voice characteristics, including pitch, tone, frequency, amplitude, and speech rate. This system utilizes advanced digital signal processing and machine learning techniques to detect these variations and classify stress levels accurately. The process begins with capturing voice input through a microphone or audio file, followed by preprocessing to remove noise and enhance signal quality. Key features such as Mel-Frequency Cepstral Coefficients (MFCC), jitter, shimmer, pitch, and energy are extracted to represent speech patterns. These features are then analyzed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and deep learning models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The system is designed with a modular architecture, ensuring scalability, efficiency, and real-time performance. It provides fast and accurate results while being user-friendly and adaptable to multiple languages and environments. The proposed system overcomes limitations of traditional stress detection methods, which are often intrusive and time-consuming. VSDS has wide applications in healthcare, security, call centers, and human-computer interaction. Overall, the system offers a reliable, cost-effective, and noninvasive approach for stress detection and contributes to advancements in emotion recognition technologies.