Automated Voice Pathology Diagnosis Through Feature Extraction
S Gayathri., E. Priya, M Manish, P S Senthil Srinivas, Sunil Richard S · 2024
This work focuses on classifying voice disorders into healthy and unhealthy categories, specifically addressing diseases such as Gastroesophageal Reflux Disease (GERD) and monochondriosis. Utilizing MATLAB and Python, we have developed an entirely software-based solution for analyzing voice signals. The system captures and processes voice recordings to identify and diagnose voice-related problems. Advanced algorithms are employed to analyse various parameters of the voice signal, such as pitch, amplitude, and frequency content. By comparing these parameters against standard values, the system can accurately classify the voice as healthy or unhealthy. This classification is crucial for early detection and intervention, potentially improving patient outcomes. This non-invasive approach offers significant improvements in the quality of life for patients with GERD, monochondriosis, and other voice disorders. By providing a reliable and efficient tool for voice analysis, this work contributes to better diagnosis and management of voice-related health issues, ushering in a new era of compassionate healthcare.