Text Normalization Through Neural Models in Generating Text Summary for Various Speech Synthesis Applications

Abstract

Machine learning , like neural network methods, has been implemented in the natural language processing of virtually every domain. With speech utilizations like vocabulary to speech organization, coalescence challenge that has been adequately immune directly toward successful machine attainments learning approaches is fundamentals normalization. Considering example, in this application it must be determined that 123 is verbalized in signatures as one hundred and twenty-three but in sovereign potentate Ave as one twenty-three. Modern industrial systems for this role are heavily dependent on hand-recorded penned double speak-specific stratification. We introduce neural interconnection miniatures well-known regard text notarization for as a streak to progression problem, where input is admission taking in token in history, and turn out gain would be that token’s verbalization.