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.