Prediction of Syllable Duration Using Structure Optimised Cuckoo Search Neural Network (SOCNN) for Text-To-Speech

Abstract

A Feed Forward Neural Network (FFNN) model primarily based unrestricted delivery prediction of language unit length pattern info speech synthesis system is that the focus of this paper. Estimation of delivery parameter of segmental length plays a essential half in unrestricted concatenative synthesis Text To Speech System (TTS) is capable of synthesize natural sounding speech with improved quality. Common options to coach the Neural Network enclosed language unit position within the phrase, context of language unit, language unit position within the word, language unit nucleus and amp; language unit identity square measure extracted from the text. Back-propagation Neural Network (BPNN) formula is one in every of the foremost wide used and a preferred technique to optimize the feed forward neural network coaching in delivery prediction. For enhance the accuracy of delivery prediction language unit length in neural BP, that’s Cuckoo Search formula to seek out the structure of the neural network with least weights while not compromising on the prediction error is planned. Speech information is adopted to check the length prediction performance of planned SOCNN, wherever the obtained results demonstrate a marked improvement over the essential BP. The system performance is shown mistreatment the synthesizing natural sounding speech for Tamil, national language of Republic of India.