Konkani Script to Speech Conversion by Concatenation of recognized Hand written Konkani Text Using Neural Network
Ramita Pradeep Karpe, Nagaraj K. Vernekar · 2019
This paper demonstrates a system for transforming the text inscribed in Konkani dialect into Speech by utilizing artificial neural network. Many visually challenged individuals use Text to Speech framework as a tool for communication. The ability to convert text to voice lessens the reliance, dissatisfaction, and feeling of defenselessness of these individuals. India is called as the land of unity and diversity, there are 22 official languages. TTS frameworks are mostly accessible in English; in any case, it has been watched that individuals feel more comfortable in hearing their own native dialect. Handwritten optical character recognition is the most challenging research zone, because of its intricacy in segmenting the character that grows on account of Devnagari Script because of Modifiers and compound characters. The document comprising Konkani text is scanned and fed to the system. In this framework the character recognition is done by utilizing Neural Network, in this manner the structure can be upgraded to work with letters written in different styles. After the characters in the Documents are viably recognized by neural network, it is composed to a text document, the entered text document is analyzed, the syllabification is accomplished in view of the phonological guidelines and the syllables are secured autonomously. At that point the syllable coordinating speech file is linked and the silence existing in the linked discourse is confined. breaks within the discourse are removed at syllable limits without diminishing the superiority of speech.