  This paper proposes speaker-adaptive neural vocoders for parametric
text-to-speech (TTS) systems. Recently proposed WaveNet-based neural vocoding
systems successfully generate a time sequence of speech signal with an
autoregressive framework. However, it remains a challenge to synthesize
high-quality speech when the amount of a target speaker's training data is
insufficient. To generate more natural speech signals with the constraint of
limited training data, we propose a speaker adaptation task with an effective
variation of neural vocoding models. In the proposed method, a
speaker-independent training method is applied to capture universal attributes
embedded in multiple speakers, and the trained model is then optimized to
represent the specific characteristics of the target speaker. Experimental
results verify that the proposed TTS systems with speaker-adaptive neural
vocoders outperform those with traditional source-filter model-based vocoders
and those with WaveNet vocoders, trained either speaker-dependently or
speaker-independently. In particular, our TTS system achieves 3.80 and 3.77 MOS
for the Korean male and Korean female speakers, respectively, even though we
use only ten minutes' speech corpus for training the model.
