  We investigated the impact of noisy linguistic features on the performance of
a Japanese speech synthesis system based on neural network that uses WaveNet
vocoder. We compared an ideal system that uses manually corrected linguistic
features including phoneme and prosodic information in training and test sets
against a few other systems that use corrupted linguistic features. Both
subjective and objective results demonstrate that corrupted linguistic
features, especially those in the test set, affected the ideal system's
performance significantly in a statistical sense due to a mismatched condition
between the training and test sets. Interestingly, while an utterance-level
Turing test showed that listeners had a difficult time differentiating
synthetic speech from natural speech, it further indicated that adding noise to
the linguistic features in the training set can partially reduce the effect of
the mismatch, regularize the model, and help the system perform better when
linguistic features of the test set are noisy.
