Automatic source speaker selection for voice conversion

This paper focuses on the importance of source speaker selection for a weighted codebook mapping based voice conversion algorithm. First, the dependency on source speakers is evaluated in a subjective listening test using 180 different source-target pairs from a database of 20 speakers. Subjective scores for similarity to target speaker's voice and quality are obtained. Statistical analysis of scores confirms the dependence of performance on source speakers for both male-to-male and female-to-female transformations. A source speaker selection algorithm is devised given a target speaker and a set of source speaker candidates. For this purpose, an artificial neural network (ANN) is trained that learns the regression between a set of acoustical distance measures and the subjective scores. The estimated scores are used in source speaker ranking. The average cross-correlation coefficient between rankings obtained from median subjective scores and rankings estimated by the algorithm is 0.84 for similarity and 0.78 for quality in male-to-male transformations. The results for female-to-female transformations were less reliable with a cross-correlation value of 0.58 for both similarity and quality.