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