Gelp: GAN-excited linear prediction for speech synthesis from mel-spectrogram

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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.

Original languageEnglish
Title of host publicationProceedings of Interspeech
PublisherInternational Speech Communication Association
Number of pages5
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInterspeech - Graz, Austria
Duration: 15 Sep 201919 Sep 2019

Publication series

NameInterspeech - Annual Conference of the International Speech Communication Association
ISSN (Electronic)2308-457X


Internet address


  • GAN
  • Neural vocoder
  • Source-filter model
  • WaveNet

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    Mikko Hakala (Manager)

    School of Science

    Facility/equipment: Facility

  • Cite this

    Juvela, L., Bollepalli, B., Yamagishi, J., & Alku, P. (2019). Gelp: GAN-excited linear prediction for speech synthesis from mel-spectrogram. In Proceedings of Interspeech (Vol. 2019-September, pp. 694-698). (Interspeech - Annual Conference of the International Speech Communication Association). International Speech Communication Association.