Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks

Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/8718

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DC Field	Value	Language
dc.contributor.author	Koolagudi, S.G.
dc.contributor.author	Vishwanath, B.K.
dc.contributor.author	Akshatha, M.
dc.contributor.author	Murthy, Y.V.S.
dc.date.accessioned	2020-03-30T10:22:37Z	-
dc.date.available	2020-03-30T10:22:37Z	-
dc.date.issued	2017
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2017, Vol.469, , pp.275-280	en_US
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/8718	-
dc.description.abstract	Voice Conversion is a technique in which source speakers voice is morphed to a target speakers voice by learning source�target relationship from a number of utterances from source and the target. There are many applications which may benefit from this sort of technology for example dubbing movies, TV-shows, TTS systems and so on. In this paper, analysis on the performance of ANN-based Voice Conversion system is done using linear predictive coding (LPC) and mel-frequency cepstral coefficients (MFCCs). Experimental results show that Voice Conversion system based on LPC features is better than the ones based on MFCC features. � Springer Science+Business Media Singapore 2017.	en_US
dc.title	Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks	en_US
dc.type	Book chapter	en_US
Appears in Collections:	2. Conference Papers

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