Deep Machine Learning-based Analysis for Intelligent Phonetic Language Recognition

Abstract

Modern speech generating systems can produce results that are almost as visually realistic as actual sounds. They still require further production management. This research presents a paradigm for managing prosodic output using explicit, unambiguous, and understandable parameters. We utilize this strategy to emphasize key words and provide a variety of architectural possibilities based on a richness of labelled resources. In an objective voice, we compare the options for producing data with or without labels. We assess them using listening tests that demonstrate our ability to retain the same level of naturalness while effectively attaining regulated concentration over a specific area.