Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8718
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dc.contributor.authorKoolagudi, S.G.
dc.contributor.authorVishwanath, B.K.
dc.contributor.authorAkshatha, M.
dc.contributor.authorMurthy, Y.V.S.
dc.date.accessioned2020-03-30T10:22:37Z-
dc.date.available2020-03-30T10:22:37Z-
dc.date.issued2017
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2017, Vol.469, , pp.275-280en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8718-
dc.description.abstractVoice 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.titlePerformance analysis of LPC and MFCC features in voice conversion using artificial neural networksen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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