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|Title:||Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks|
|Citation:||Advances in Intelligent Systems and Computing, 2017, Vol.469, , pp.275-280|
|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.|
|Appears in Collections:||2. Conference Papers|
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