  Recently the state-of-the-art text-to-speech synthesis systems have shifted
to a two-model approach: a sequence-to-sequence model to predict a
representation of speech (typically mel-spectrograms), followed by a 'neural
vocoder' model which produces the time-domain speech waveform from this
intermediate speech representation. This approach is capable of synthesizing
speech that is confusable with natural speech recordings. However, the
inference speed of neural vocoder approaches represents a major obstacle for
deploying this technology for commercial applications. Parallel WaveNet is one
approach which has been developed to address this issue, trading off some
synthesis quality for significantly faster inference speed. In this paper we
investigate the use of a sentence-level conditioning vector to improve the
signal quality of a Parallel WaveNet neural vocoder. We condition the neural
vocoder with the latent vector from a pre-trained VAE component of a Tacotron
2-style sequence-to-sequence model. With this, we are able to significantly
improve the quality of vocoded speech.
