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DC Field | Value | Language |
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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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