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Recently the state-of-the-art text-to-speech synthesis systems have shiftedto a two-model approach: a sequence-to-sequence model to predict arepresentation of speech (typically mel-spectrograms), followed by a 'neuralvocoder' model which produces the time-domain speech waveform from thisintermediate speech representation. This approach is capable of synthesizingspeech that is confusable with natural speech recordings. However, theinference speed of neural vocoder approaches represents a major obstacle fordeploying this technology for commercial applications. Parallel WaveNet is oneapproach which has been developed to address this issue, trading off somesynthesis quality for significantly faster inference speed. In this paper weinvestigate the use of a sentence-level conditioning vector to improve thesignal quality of a Parallel WaveNet neural vocoder. We condition the neuralvocoder with the latent vector from a pre-trained VAE component of a Tacotron2-style sequence-to-sequence model. With this, we are able to significantlyimprove the quality of vocoded speech.

Parallel WaveNet conditioned on VAE latent vectors