Speech Processing on Detection of Respiratory Disease

Abstract

Abstract: Speech processing applied to the detection of respiratory diseases has emerged as an innovative and groundbreaking field in the medical domain. By analyzing audio signals, specifically cough and respiratory sounds, this cutting-edge approach offers a non-invasive and costefficient method for early diagnosis and continuous monitoring of various respiratory conditions. Herein we report a review on the process entailing extraction of crucial features like Mel-Frequency Cepstral Coefficients (MFCCs) and cochlegram image characteristics, inputting the subsequent ones advanced machine learning techniques like Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) for accurate classification. Despite encouraging outcomes, certain challenges, including limited data availability and variability in cough sounds, pose obstacles to further advancements. Nevertheless, dedicated researchers are actively working on expanding datasets, bolstering the robustness of algorithms, and integrating multimodal data to surmount these hurdles. The potential benefits of speech processing in respiratory disease detection are vast, encompassing prompt identification, remote assessment capabilities, and personalized medical interventions. Continued collaboration between engineers, healthcare professionals, and patients is essential to fully harness the potential of this technology in revolutionizing respiratory disease diagnosis and improving patient care.