We present a TTS neural network that is able to produce speech in multiple languages. The proposed network is able to transfer a voice, which was presented as a sample in a source language, into one of several target languages. Training is done without using matching or parallel data, i.e., without samples of the same speaker in multiple languages, making the method much more applicable. The conversion is based on learning a polyglot network that has multiple per-language sub-networks and adding loss terms that preserve the speaker’s identity in multiple languages. We evaluate the proposed polyglot neural network for three languages with a total of more than 400 speakers and demonstrate convincing conversion capabilities.
We have successfully proposed to use multi-speaker modelling in DNN-based TTS synthesis for improved voice quality with limited available data from a speaker. In this paper, we propose a new speaker and language factorized DNN, where speaker-specific layers are used for multi-speaker modelling, and shared layers and language-specific layers are employed for multi-language, linguistic feature transformation. Experimental results on a speech corpus of multiple speakers in both Mandarin and English show that the proposed factorized DNN can not only achieve a similar voice quality as that of a multi-speaker DNN, but also perform polyglot synthesis with a monolingual speaker's voice.