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A Novel Approach for Hindi Text Description to Speech and Expressive Speech Synthesis

Kamble Kaveri, Ramesh Kagalkar Published in Signal Processing

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
10.5120/ijais15-451342
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  1. Kamble Kaveri and Ramesh Kagalkar. Article: A Novel Approach for Hindi Text Description to Speech and Expressive Speech Synthesis. International Journal of Applied Information Systems 8(7):1-5, May 2015. BibTeX

    @article{key:article,
    	author = "Kamble Kaveri and Ramesh Kagalkar",
    	title = "Article: A Novel Approach for Hindi Text Description to Speech and Expressive Speech Synthesis",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 7,
    	pages = "1-5",
    	month = "May",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Communication plays a very important role in every days life. With the help of communication we can share information from one person to another. Speech is the primary means of communication. A Text to Speech (TTS) synthesizer is a computer based application which is capable of given reading out to the typed text. This generally forms basic two steps, such as text processing and speech generation. Our aim is to develop software that enhances the users way of speech through correctness of pronunciation for the Hindi phonetics. Firstly the simple TTS system is to perform operation to get the output in the form of Text for Hindi language. Then Speech to Text (STT) conversion may form effectively. Additionally we have to add Expressions for Expressive Speech synthesis for Hindi Language. TTS is one of the major applications of NLP. Expressive speech synthesis deals with synthesizing speech and adding various expressions related to different emotions and speaking styles to the synthesized speech. Emotion is an important element in expressive speech synthesis.

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Keywords

Text To Speech ; Speech To Text; Boosting-Gaussian Mixture Model(GMM); Mel Frequency Cepstral Coefficient (MFCC); Prosody Conversion; Hidden Markov Model(HMM); Time Domain Pitch Synchronous Overlap Add(TD-PSOLA).