Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis

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Abstract

Recent studies have shown that text-to-speech synthesis quality can be improved by using glottal vocoding. This refers to vocoders that parameterize speech into two parts, the glottal excitation and vocal tract, that occur in the human speech production apparatus. Current glottal vocoders generate the glottal excitation waveform by using deep neural networks (DNNs). However, the squared error-based training of the present glottal excitation models is limited to generating conditional average waveforms, which fails to capture the stochastic variation of the waveforms. As a result, shaped noise is added as post-processing. In this study, we propose a new method for predicting glottal waveforms by generative adversarial networks (GANs). GANs are generative models that aim to embed the data distribution in a latent space, enabling generation of new instances very similar to the original by randomly sampling the latent distribution. The glottal pulses generated by GANs show a stochastic component similar to natural glottal pulses. In our experiments, we compare synthetic speech generated using glottal waveforms produced by both DNNs and GANs. The results show that the newly proposed GANs achieve synthesis quality comparable to that of widely-used DNNs, without using an additive noise component.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
PublisherInternational Speech Communication Association
Pages3394-3398
Number of pages5
Volume2017-August
ISBN (Print)978-1-5108-4876-4
DOIs
Publication statusPublished - Aug 2017
MoE publication typeA4 Article in a conference publication
EventInterspeech - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017
Conference number: 18
http://www.interspeech2017.org/

Publication series

NameInterspeech: Annual Conference of the International Speech Communication Association
ISSN (Electronic)1990-9772

Conference

ConferenceInterspeech
CountrySweden
CityStockholm
Period20/08/201724/08/2017
Internet address

Keywords

  • Glottal souce modelling
  • GAN
  • TTS
  • DNN

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  • Cite this

    Bollepalli, B., Juvela, L., & Alku, P. (2017). Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (Vol. 2017-August, pp. 3394-3398). (Interspeech: Annual Conference of the International Speech Communication Association). International Speech Communication Association. https://doi.org/10.21437/Interspeech.2017-1288