Cross-language voice conversion based on eigenvoices

This paper presents a novel cross-language voice conversion (VC) method based on eigenvoice conversion (EVC). Crosslanguage VC is a technique for converting voice quality between two speakers uttering different languages each other. In general, parallel data consisting of utterance pairs of those two speakers are not available. To deal with this problem, we apply EVC to cross-language VC. First, we train an eigenvoice GMM (EV-GMM) using many parallel data sets by a source speaker and many pre-stored other speakers who can utter the same language as the source speaker. And then, the conversion model between the source speaker and a target speaker who cannot utter the source speaker’s language is developed by adapting the EV-GMM using a few arbitrary sentences uttered by the target speaker in a different language. The experimental results demonstrate that the proposed method yields significant performance improvements in both speech quality and conversion accuracy for speaker individuality compared with a conventional cross-language VC method based on frame selection.