An Implementation of Advanced NLP for High-Quality Text-To-Speech Synthesis

Abstract

In this paper, we utilize Bengali voice information (Bangladesh and Kolkata) to convert it in to text format. In order to build a Speech to text conversion framework one should give two key parts: a NLP (Natural Language Processing) stage, basically works on the information on the input speech, and a text generation stage to produce the desired output. These two distinct levels must exchange both data and commands to supply Text. As completing task relies on many distinct scientific areas, any achievement toward standardization can minimize the effort and increase the dynamic of the results. The development in correspondence advancements AI (specially Machine learning and Deep learning) led researcher in convolutional neural network (CNN), which is standing out enough to be noticed because of its high performance. Nonetheless, most normal issue with deep learning architectures such as CNN is that they require large amount of data for training. This paper gives an overview of the NLP stage in the speech to txt framework for Bangla language built by our collective, and describes the integration into the database.