  Previous work on speaker adaptation for end-to-end speech synthesis still
falls short in speaker similarity. We investigate an orthogonal approach to the
current speaker adaptation paradigms, speaker augmentation, by creating
artificial speakers and by taking advantage of low-quality data. The base
Tacotron2 model is modified to account for the channel and dialect factors
inherent in these corpora. In addition, we describe a warm-start training
strategy that we adopted for Tacotron2 training. A large-scale listening test
is conducted, and a distance metric is adopted to evaluate synthesis of
dialects. This is followed by an analysis on synthesis quality, speaker and
dialect similarity, and a remark on the effectiveness of our speaker
augmentation approach. Audio samples are available online.
