alexa Abstract | Duration Modeling For Telugu Language with Recurrent Neural Network
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Research Article Open Access


In this paper, a novel syllable duration modeling approach for Telugu speech is proposed. Duration of a syllable is influenced by positional and contextual variations of syllables. Multiple linguistic features of syllables at different levels like positional and contextual features are used from text. Duration values of syllables are extracted from speech analysis software PRAAT. Duration of a syllable is predicted by a Recurrent Neural Network (RNN) algorithm. A small speech database is considered as a preliminary work to predict syllable duration with proposed RNN algorithm. Experiments are conducted with different sets of features.

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Author(s): V.S.Ramesh Bonda, P.N.Girija


duration, speech synthesis, recurrent neural networks, syllables, Parts of Speech, Positional and contextual features, Speech Therapy for Adults,Speech and Language pathology,Bilingual Speech pathology,Speech and Language Disorders

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