Conditional Vector Quantization for Voice Conversion

Abstract

Voice conversion methods have the objective of transforming speech spoken by a particular source speaker, so that it sounds as if spoken by a different target speaker. The majority of voice conversion methods is based on transforming the short-time spectral envelope of the source speaker, based on derived correspondences between the source and target vectors using training speech data from both speakers. These correspondences are usually obtained by segmenting the spectral vectors of one or both speakers into clusters, using soft (GMM-based) or hard (VQ-based) clustering. Here, we propose that voice conversion performance can be improved by taking advantage of the fact that often the relationship between the source and target vectors is one-to-many. In order to illustrate this, we propose that a VQ approach namely constrained vector quantization (CVQ), can be used for voice conversion. Results indicate that indeed such a relationship between the source and target data exists and can be exploited by following a CVQ-based function for voice conversion.