A comparison of Multi-Layer Perceptron and Radial Basis Function neural network in the voice conversion framework

@article{Chadha2014ACO,
  title={A comparison of Multi-Layer Perceptron and Radial Basis Function neural network in the voice conversion framework},
  author={Ankita N. Chadha and Jagannath H. Nirmal and Mukesh A. Zaveri},
  journal={2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)},
  year={2014},
  pages={1045-1052}
}
The voice conversion system modifies the speaker specific features of the source speaker so that it sounds like a target speaker speech. The voice individuality of the speech signal is characterized at various levels such as shape of the glottal excitation, shape of the vocal tract and the long term prosodic features. In this work, Line Spectral Frequencies (LSF) are used to represent the shape of the vocal tract and Linear Predictive (LP) residual represents the shape of the glottal excitation… CONTINUE READING

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