Voice-Based Digital Biomarkers for Remote Health Assessment: A Machine Learning Perspective

Abstract

The rapid evolution of artificial intelligence (AI) and digital health technologies has significantly transformed healthcare delivery, particularly in remote and telemedicine-based settings. Among emerging innovations, voice-based digital biomarkers have gained substantial attention due to their non-invasive, cost-effective, and scalable nature. These biomarkers leverage acoustic and linguistic features extracted from human speech to infer physiological, neurological, and psychological conditions. This paper provides a comprehensive machine learning perspective on voice-based digital biomarkers for remote health assessment. It explores the theoretical foundations of voice as a biomarker, data acquisition methodologies, feature engineering techniques, and advanced machine learning and deep learning models used for disease detection and monitoring. Recent studies demonstrate that variations in speech patterns can reflect underlying conditions such as Parkinson’s disease, depression, cardiovascular disorders, and respiratory illnesses. Machine learning models, including support vector machines, random forests, and deep neural networks, have shown promising performance in classifying and predicting health states based on voice data. Furthermore, the integration of voice biomarkers into mobile and telehealth platforms enables continuous monitoring and early detection, especially in resource-constrained environments. Despite significant progress, challenges remain in terms of data standardization, model generalizability, privacy concerns, and regulatory   approval. This paper critically evaluates these challenges and proposes future research directions to enhance the reliability and clinical applicability of voice-based diagnostic systems. The study aims to bridge the gap between technological innovation and clinical implementation, offering a roadmap for scalable and ethical deployment of AI-driven voice biomarker systems.