Articulatory Text-to-Speech Synthesis Using the Digital Waveguide Mesh Driven by a Deep Neural Network

Amelia J. Gully, Takenori Yoshimura, Damian T. Murphy, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda


Following recent advances in direct modeling of the speech waveform using a deep neural network, we propose a novel method that directly estimates a physical model of the vocal tract from the speech waveform, rather than magnetic resonance imaging data. This provides a clear relationship between the model and the size and shape of the vocal tract, offering considerable flexibility in terms of speech characteristics such as age and gender. Initial tests indicate that despite a highly simplified physical model, intelligible synthesized speech is obtained. This illustrates the potential of the combined technique for the control of physical models in general, and hence the generation of more natural-sounding synthetic speech.


 DOI: 10.21437/Interspeech.2017-900

Cite as: Gully, A.J., Yoshimura, T., Murphy, D.T., Hashimoto, K., Nankaku, Y., Tokuda, K. (2017) Articulatory Text-to-Speech Synthesis Using the Digital Waveguide Mesh Driven by a Deep Neural Network. Proc. Interspeech 2017, 234-238, DOI: 10.21437/Interspeech.2017-900.


@inproceedings{Gully2017,
  author={Amelia J. Gully and Takenori Yoshimura and Damian T. Murphy and Kei Hashimoto and Yoshihiko Nankaku and Keiichi Tokuda},
  title={Articulatory Text-to-Speech Synthesis Using the Digital Waveguide Mesh Driven by a Deep Neural Network},
  year=2017,
  booktitle={Proc. Interspeech 2017},
  pages={234--238},
  doi={10.21437/Interspeech.2017-900},
  url={http://dx.doi.org/10.21437/Interspeech.2017-900}
}