Please use this identifier to cite or link to this item:
https://idr.l2.nitk.ac.in/jspui/handle/123456789/8718
Title: | Performance analysis of LPC and MFCC features in voice conversion using artificial neural networks |
Authors: | Koolagudi, S.G. Vishwanath, B.K. Akshatha, M. Murthy, Y.V.S. |
Issue Date: | 2017 |
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. |
URI: | http://idr.nitk.ac.in/jspui/handle/123456789/8718 |
Appears in Collections: | 2. Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.