Optimizing Point of Care Interaction with Speech Recognition to Preserve Learnability and Intelligibility
Sanjay Kumar Suman, Sandeep Pandey, Vikas Kumar, Subha Balamurugan, Madhavi Pingili, Paresh Tanna
Abstract
The transmission of information via a variety of channels, such as spoken, written, and visual interaction, has been a defining characteristic of the trajectory of human development. The manner in which information is sent has a substantial influence on the retention and comprehension of information, especially in situations where context-specific, based on classes, and semantic connections are at play. The interpolation method algorithms have advanced to the point where they allow for the preservation of data usefulness and integrity while simultaneously guaranteeing semantic congruence and confidentiality. In a similar manner, semantic analysis is performed on text that has been extracted from photographs in order to analyse the parallels in composition and interpretation. A further factor that contributes to the complexity of auditory and textual features is the presence of distinctive dialectal tendencies and climatic fluctuations, both of which provide difficulties for algorithmic learners. The incorporation of prosody into learnt variables and taxonomies makes it easier to find interesting things inside data, despite the difficulties that are involved. This ontology-driven method provides a solid foundation for cutting-edge technology and interoperability due to the fact that it incorporates insights from text, audio, and graphical data. The framework that has been suggested enables smooth information distribution, better privacy, and semantic comprehension by synthesized multiple modalities of data. When compared to other approaches, the techniques that were recommended perform very well in all four criteria and have an accuracy rate of 92.8%.