  End-to-end speech synthesis methods already achieve close-to-human quality
performance. However compared to HMM-based and NN-based frame-to-frame
regression methods, they are prone to some synthesis errors, such as missing or
repeating words and incomplete synthesis. We attribute the comparatively high
utterance error rate to the local information preference of conditional
autoregressive models, and the ill-posed training objective of the model, which
describes mostly the training status of the autoregressive module, but rarely
that of the condition module. Inspired by InfoGAN, we propose to maximize the
mutual information between the text condition and the predicted acoustic
features to strengthen the dependency between them for CAR speech synthesis
model, which would alleviate the local information preference issue and reduce
the utterance error rate. The training objective of maximizing mutual
information can be considered as a metric of the dependency between the
autoregressive module and the condition module. Experiment results show that
our method can reduce the utterance error rate.
