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In this paper we propose a new cross-lingual Voice Conversion (VC) approachwhich can generate all speech parameters (MCEP, LF0, BAP) from one DNN modelusing PPGs (Phonetic PosteriorGrams) extracted from inputted speech usingseveral ASR acoustic models. Using the proposed VC method, we tried threedifferent approaches to build a multilingual TTS system without recording amultilingual speech corpus. A listening test was carried out to evaluate bothspeech quality (naturalness) and voice similarity between converted speech andtarget speech. The results show that Approach 1 achieved the highest level ofnaturalness (3.28 MOS on a 5-point scale) and similarity (2.77 MOS).

Building Multi lingual TTS using Cross Lingual Voice Conversion