  Recently, synthesizing personalized speech by text-to-speech (TTS)
application is highly demanded. But the previous TTS models require a mass of
target speaker speeches for training. It is a high-cost task, and hard to
record lots of utterances from the target speaker. Data augmentation of the
speeches is a solution but leads to the low-quality synthesis speech problem.
Some multi-speaker TTS models are proposed to address the issue. But the
quantity of utterances of each speaker imbalance leads to the voice similarity
problem. We propose the Target Domain Adaptation Speech Synthesis Network
(TDASS) to address these issues. Based on the backbone of the Tacotron2 model,
which is the high-quality TTS model, TDASS introduces a self-interested
classifier for reducing the non-target influence. Besides, a special gradient
reversal layer with different operations for target and non-target is added to
the classifier. We evaluate the model on a Chinese speech corpus, the
experiments show the proposed method outperforms the baseline method in terms
of voice quality and voice similarity.
