  We introduce an approach to multilingual speech synthesis which uses the
meta-learning concept of contextual parameter generation and produces
natural-sounding multilingual speech using more languages and less training
data than previous approaches. Our model is based on Tacotron 2 with a fully
convolutional input text encoder whose weights are predicted by a separate
parameter generator network. To boost voice cloning, the model uses an
adversarial speaker classifier with a gradient reversal layer that removes
speaker-specific information from the encoder.
  We arranged two experiments to compare our model with baselines using various
levels of cross-lingual parameter sharing, in order to evaluate: (1) stability
and performance when training on low amounts of data, (2) pronunciation
accuracy and voice quality of code-switching synthesis. For training, we used
the CSS10 dataset and our new small dataset based on Common Voice recordings in
five languages. Our model is shown to effectively share information across
languages and according to a subjective evaluation test, it produces more
natural and accurate code-switching speech than the baselines.
