Performance Analysis of LPC and MFCC Features in Voice Conversion Using Artificial Neural Networks

Shashidhar G Koolagudi,B Kavya Vishwanath,Yarlagadda V S Murthy,M Akshatha

doi:10.1007/978-981-10-1678-3_27

Performance Analysis of LPC and MFCC Features in Voice Conversion Using Artificial Neural Networks

Shashidhar G Koolagudi, B Kavya Vishwanath + Show 2 more

https://doi.org/10.1007/978-981-10-1678-3_27

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Publication Date: Aug 25, 2016

Citations: 4

Affiliation: National Institute of Technology Karnataka

#Linear Predictive Coding Features #Linear Predictive Coding + Show 8 more

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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.

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