  Personalizing a speech synthesis system is a highly desired application,
where the system can generate speech with the user's voice with rare enrolled
recordings. There are two main approaches to build such a system in recent
works: speaker adaptation and speaker encoding. On the one hand, speaker
adaptation methods fine-tune a trained multi-speaker text-to-speech (TTS) model
with few enrolled samples. However, they require at least thousands of
fine-tuning steps for high-quality adaptation, making it hard to apply on
devices. On the other hand, speaker encoding methods encode enrollment
utterances into a speaker embedding. The trained TTS model can synthesize the
user's speech conditioned on the corresponding speaker embedding. Nevertheless,
the speaker encoder suffers from the generalization gap between the seen and
unseen speakers.
  In this paper, we propose applying a meta-learning algorithm to the speaker
adaptation method. More specifically, we use Model Agnostic Meta-Learning
(MAML) as the training algorithm of a multi-speaker TTS model, which aims to
find a great meta-initialization to adapt the model to any few-shot speaker
adaptation tasks quickly. Therefore, we can also adapt the meta-trained TTS
model to unseen speakers efficiently. Our experiments compare the proposed
method (Meta-TTS) with two baselines: a speaker adaptation method baseline and
a speaker encoding method baseline. The evaluation results show that Meta-TTS
can synthesize high speaker-similarity speech from few enrollment samples with
fewer adaptation steps than the speaker adaptation baseline and outperforms the
speaker encoding baseline under the same training scheme. When the speaker
encoder of the baseline is pre-trained with extra 8371 speakers of data,
Meta-TTS can still outperform the baseline on LibriTTS dataset and achieve
comparable results on VCTK dataset.
