  We present a meta-learning approach for adaptive text-to-speech (TTS) with
few data. During training, we learn a multi-speaker model using a shared
conditional WaveNet core and independent learned embeddings for each speaker.
The aim of training is not to produce a neural network with fixed weights,
which is then deployed as a TTS system. Instead, the aim is to produce a
network that requires few data at deployment time to rapidly adapt to new
speakers. We introduce and benchmark three strategies: (i) learning the speaker
embedding while keeping the WaveNet core fixed, (ii) fine-tuning the entire
architecture with stochastic gradient descent, and (iii) predicting the speaker
embedding with a trained neural network encoder. The experiments show that
these approaches are successful at adapting the multi-speaker neural network to
new speakers, obtaining state-of-the-art results in both sample naturalness and
voice similarity with merely a few minutes of audio data from new speakers.
