  This paper introduces an improved generative model for statistical parametric
speech synthesis (SPSS) based on WaveNet under a multi-task learning framework.
Different from the original WaveNet model, the proposed Multi-task WaveNet
employs the frame-level acoustic feature prediction as the secondary task and
the external fundamental frequency prediction model for the original WaveNet
can be removed. Therefore the improved WaveNet can generate high-quality speech
waveforms only conditioned on linguistic features. Multi-task WaveNet can
produce more natural and expressive speech by addressing the pitch prediction
error accumulation issue and possesses more succinct inference procedures than
the original WaveNet. Experimental results prove that the SPSS method proposed
in this paper can achieve better performance than the state-of-the-art approach
utilizing the original WaveNet in both objective and subjective preference
tests.
