  Neural end-to-end TTS can generate very high-quality synthesized speech, and
even close to human recording within similar domain text. However, it performs
unsatisfactory when scaling it to challenging test sets. One concern is that
the encoder-decoder with attention-based network adopts autoregressive
generative sequence model with the limitation of "exposure bias" To address
this issue, we propose two novel methods, which learn to predict future by
improving agreement between forward and backward decoding sequence. The first
one is achieved by introducing divergence regularization terms into model
training objective to reduce the mismatch between two directional models,
namely L2R and R2L (which generates targets from left-to-right and
right-to-left, respectively). While the second one operates on decoder-level
and exploits the future information during decoding. In addition, we employ a
joint training strategy to allow forward and backward decoding to improve each
other in an interactive process. Experimental results show our proposed methods
especially the second one (bidirectional decoder regularization), leads a
significantly improvement on both robustness and overall naturalness, as
outperforming baseline (the revised version of Tacotron2) with a MOS gap of
0.14 in a challenging test, and achieving close to human quality (4.42 vs. 4.49
in MOS) on general test.
