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

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 of a particular speaker. A Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) based neural network are explored to formulate the nonlinear mapping for modifying the LSFs. The baseline residual selection method is used to modify the LP-residual of one speaker to that of another speaker. A relative comparison between MLP and RBF are carried out using various objective and subjective measures for inter-gender and intra-gender voice conversion. The results reveal that an optimized RBF performs slightly better than baseline MLP based voice conversion.