  Neural networks with Auto-regressive structures, such as Recurrent Neural
Networks (RNNs), have become the most appealing structures for acoustic
modeling of parametric text to speech synthesis (TTS) in ecent studies. Despite
the prominent capacity to capture long-term dependency, these models consist of
massive sequential computations that cannot be fully parallel. In this paper,
we propose a U-shaped Fully-parallel Acoustic Neural Structure (UFANS), which
is a deconvolutional alternative of RNNs for Statistical Parametric Speech
Synthesis (SPSS). The experiments verify that our proposed model is over 20
times faster than RNN based acoustic model, both training and inference on GPU
with comparable speech quality. Furthermore, We also investigate that how long
information dependence really matters to synthesized speech quality.
