Speech technology in healthcare

Abstract

As the population ages with advances in technology, health monitoring through early detection is increasing. There are several approaches to the analysis, monitoring and management of human activity in health care. Gaining proficiency and knowledge from complicated, multidimensional, and dissimilar biomedical data remains a key challenge for healthcare innovation. Different kinds of data have been appearing in modern biomedical research. Recent advances in deep learning technology provide a powerful new paradigm for deriving end to end learning models out of complicated data. Speech technology is anticipated to lead to changes in health care, transforming traditional treatment modalities. The present language interface is being transformed into a next generation medical companion who interacts among people and monitors their health, according to researchers. New genuine mechanisms are required to reinforce the medical prognosis of the diseases and lessen the hour spent in the diagnostic process. This allows speech to be used as a future digital biomarker for disease detection, with benefits such as improved access to diagnosis, symptom monitoring, cost savings and improved diagnostic accuracy. The motive of this paper is to conduct an extensive survey related to speech technology in healthcare. Because of its societal implications, research in this subject has a broad reach.

Keywords

• Biomedical applications
• Deep learning
• Digital biomarker
• Healthcare
• Machine learning
• Speech technology