Abstract:

The paper proposes a new voice conversion method based on using Particle Swarm Optimization (PSO)to optimize General Regression Neural Network (GRNN).Firstly, the method utilizes the characteristic parameters of the training speaker’s vocal tract and source excitation to train two GRNNs, and then obtains the structure parameters of GRNNs. Secondly, in order to reduce the adverse impact of artificial maninduced factors on conversion results,  PSO is used to optimize the parameters of the GRNN model. Finally, the pitch contour and the energy profile of prosodic features are linearly converted, thus making the converted voice contain more personalized feature information of source speaker.Experimental results show that,compared with the radial basis function neural network(RBF) and the GRNN based voice conversion methods,our method improves the naturalness and likelihood of the converted voices and evidently decreases the spectral distortion rate, so the converted voices are more closed to the target voices.

Key words: voice conversion, general regression neural network model, particle swarm optimization