  We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To
Speech), a novel approach for few-shot speaker adaptation. Here, the task is to
fine-tune a pre-trained TTS model to mimic a new speaker using a small corpus
of target utterances. We demonstrate that there does not exist a
one-size-fits-all adaptation strategy, with convincing synthesis requiring a
corpus-specific configuration of the hyper-parameters that control fine-tuning.
By using Bayesian optimization to efficiently optimize these hyper-parameter
values for a target speaker, we are able to perform adaptation with an average
30% improvement in speaker similarity over standard techniques. Results
indicate, across multiple corpora, that BOFFIN TTS can learn to synthesize new
speakers using less than ten minutes of audio, achieving the same naturalness
as produced for the speakers used to train the base model.
