  This paper presents a method of decoupled pronunciation and prosody modeling
to improve the performance of meta-learning-based multilingual speech
synthesis. The baseline meta-learning synthesis method adopts a single text
encoder with a parameter generator conditioned on language embeddings and a
single decoder to predict mel-spectrograms for all languages. In contrast, our
proposed method designs a two-stream model structure that contains two encoders
and two decoders for pronunciation and prosody modeling, respectively,
considering that the pronunciation knowledge and the prosody knowledge should
be shared in different ways among languages. In our experiments, our proposed
method effectively improved the intelligibility and naturalness of multilingual
speech synthesis comparing with the baseline meta-learning synthesis method.
