Multi-scale Speaker Transformation using Radial Basis Function

摘要

In speaker transformation, the speaker dependent spectral parameters are generally characterized by single scale features. These features approximate the vocal tract, but produce artifacts during speech signal reconstruction. In this paper, multi-resolution wavelet based feature set is proposed, which finely tunes the speaker specific characteristics of the speech signal. The Radial Basis Function is used to propose the mapping function for modifying these characteristics. The performance of the proposed system is evaluated using different objective and subjective measures. Evaluation results illustrate that the proposed algorithm maintains target voice individuality while maintaining the quality and naturalness of the speech signal.