  During the last few years, spoken language technologies have known a big
improvement thanks to Deep Learning. However Deep Learning-based algorithms
require amounts of data that are often difficult and costly to gather.
Particularly, modeling the variability in speech of different speakers,
different styles or different emotions with few data remains challenging. In
this paper, we investigate how to leverage fine-tuning on a pre-trained Deep
Learning-based TTS model to synthesize speech with a small dataset of another
speaker. Then we investigate the possibility to adapt this model to have
emotional TTS by fine-tuning the neutral TTS model with a small emotional
dataset.
