Abstract: Spectrum conversion is a key technique in voice conversion. At present, most of vocal tract spectrum conversion methods are first to extract one of characteristic parameters of the vocal tract then to train and convert it, and finally to synthesize the converted voice. Since different characteristic parameters of the vocal tract characterize different physical and acoustic meanings, these methods usually ignore the possible complementary effects between different characteristic parameters. To solve this problem, this paper studies the joint modeling method between different characteristic parameters of vocal tract, and introduces a new characteristic parameter called LPC-MFCC which is composed of Linear Prediction Coefficient (LPC) and Mel-Frequency Cepstral Coefficient (MFCC). And then, a voice conversion method based on Gaussian Mixture Model (GMM) with LPC-MFCC is proposed. In order to verify the effectiveness of the proposed method, the voice conversion method based on GMM with LPC parameter is selected for comparison in simulation experiments. Subjective and objective tests are conducted with multiple sets of experimental data, and the results show that the proposed voice conversion method can achieve a higher similarity of voice conversion.