  State-of-the-art statistical parametric speech synthesis (SPSS) generally
uses a vocoder to represent speech signals and parameterize them into features
for subsequent modeling. Magnitude spectrum has been a dominant feature over
the years. Although perceptual studies have shown that phase spectrum is
essential to the quality of synthesized speech, it is often ignored by using a
minimum phase filter during synthesis and the speech quality suffers. To bypass
this bottleneck in vocoded speech, this paper proposes a phase-embedded
waveform representation framework and establishes a magnitude-phase joint
modeling platform for high-quality SPSS. Our experiments on waveform
reconstruction show that the performance is better than that of the widely-used
STRAIGHT. Furthermore, the proposed modeling and synthesis platform outperforms
a leading-edge, vocoded, deep bidirectional long short-term memory recurrent
neural network (DBLSTM-RNN)-based baseline system in various objective
evaluation metrics conducted.
