  Recent neural networks such as WaveNet and sampleRNN that learn directly from
speech waveform samples have achieved very high-quality synthetic speech in
terms of both naturalness and speaker similarity even in multi-speaker
text-to-speech synthesis systems. Such neural networks are being used as an
alternative to vocoders and hence they are often called neural vocoders. The
neural vocoder uses acoustic features as local condition parameters, and these
parameters need to be accurately predicted by another acoustic model. However,
it is not yet clear how to train this acoustic model, which is problematic
because the final quality of synthetic speech is significantly affected by the
performance of the acoustic model. Significant degradation happens, especially
when predicted acoustic features have mismatched characteristics compared to
natural ones. In order to reduce the mismatched characteristics between natural
and generated acoustic features, we propose frameworks that incorporate either
a conditional generative adversarial network (GAN) or its variant, Wasserstein
GAN with gradient penalty (WGAN-GP), into multi-speaker speech synthesis that
uses the WaveNet vocoder. We also extend the GAN frameworks and use the
discretized mixture logistic loss of a well-trained WaveNet in addition to mean
squared error and adversarial losses as parts of objective functions.
Experimental results show that acoustic models trained using the WGAN-GP
framework using back-propagated discretized-mixture-of-logistics (DML) loss
achieves the highest subjective evaluation scores in terms of both quality and
speaker similarity.
