Role of neural network models for developing speech systems |
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Authors: | K SREENIVASA RAO |
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Affiliation: | (1) Department of Computer Applications, Rajiv Gandhi Institute of Technology, Velloor P.O., 686501 Kottayam, Kerala, India |
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Abstract: | This paper discusses the application of neural networks for developing different speech systems. Prosodic parameters of speech
at syllable level depend on positional, contextual and phonological features of the syllables. In this paper, neural networks
are explored to model the prosodic parameters of the syllables from their positional, contextual and phonological features.
The prosodic parameters considered in this work are duration and sequence of pitch (F
0) values of the syllables. These prosody models are further examined for applications such as text to speech synthesis, speech
recognition, speaker recognition and language identification. Neural network models in voice conversion system are explored
for capturing the mapping functions between source and target speakers at source, system and prosodic levels. We have also
used neural network models for characterizing the emotions present in speech. For identification of dialects in Hindi, neural
network models are used to capture the dialect specific information from spectral and prosodic features of speech. |
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