A novel approach improvement framework for text to speech synthesis

Abstract

Text to speech applications are mostly used to extract interaction with the user in high-level multimedia tools. These applications usually produce artificial (robotic) sounds. In this study, instead of synthesizing textual expressions as monotone, single sound form, it is aimed to be separated into species and sounded as different sound forms. Briefly, the process of speech synthesis from the text is considered as a text classification problem. Machine learning algorithms have been used to perform this sorting process. As a result of the classification, sound files of correctly classified documents are obtained in the formats initially set as default, and different sound formats are obtained for misclassified documents except for their own category. © 2018 IEEE.