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

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dc.contributor.authorKoolagudi, S.G.
dc.contributor.authorVishwanath, B.K.
dc.contributor.authorAkshatha, M.
dc.contributor.authorVishnu Srinivasa Murthy, Y.V.S.
dc.date.accessioned2026-02-06T06:38:56Z
dc.date.issued2017
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.
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2017, Vol.469, , p. 275-280
dc.identifier.issn21945357
dc.identifier.urihttps://doi.org/10.1007/978-981-10-1678-3_27
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/31999
dc.publisherSpringer Verlag service@springer.de
dc.subjectLinear predictive coding and neural networks
dc.subjectMel-frequency cepstral coefficients
dc.subjectMorphing
dc.subjectVoice conversion
dc.titlePerformance analysis of LPC and MFCC features in voice conversion using artificial neural networks

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