An Approach to Voice Conversion Based on Non-Linear Canonical Correlation Analysis

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality. The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using non-linear canonical correlation analysis (NLCCA) based on jointed Gaussian mixture model. Speaker individuality transformation was achieved mainly by altering vocal tract characteristics represented by line spectral frequencies (LSF). To obtain the transformed speech sounded more like the target voices, prosody modification is involved through residual prediction. Both objective and subjective evaluations were conducted. The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the minimum mean square error (MMSE) estimation.