  Recent advances in neural network -based text-to-speech have reached human
level naturalness in synthetic speech. The present sequence-to-sequence models
can directly map text to mel-spectrogram acoustic features, which are
convenient for modeling, but present additional challenges for vocoding (i.e.,
waveform generation from the acoustic features). High-quality synthesis can be
achieved with neural vocoders, such as WaveNet, but such autoregressive models
suffer from slow sequential inference. Meanwhile, their existing parallel
inference counterparts are difficult to train and require increasingly large
model sizes. In this paper, we propose an alternative training strategy for a
parallel neural vocoder utilizing generative adversarial networks, and
integrate a linear predictive synthesis filter into the model. Results show
that the proposed model achieves significant improvement in inference speed,
while outperforming a WaveNet in copy-synthesis quality.
