Towards Speech Synthesis from Intracranial Signals
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Towards Speech Synthesis from Intracranial Signals (Christian Herff, Lorenz Diener, Emily Mugler, Marc Slutzky, Dean Krusienski, Tanja Schultz), chapter of "Brain--Computer Interface Research", pages 47--54, October 2020
Bibtex Entry:
  title        = {Towards Speech Synthesis from Intracranial Signals},
  author       = {Herff, Christian and Diener, Lorenz and Mugler, Emily and Slutzky, Marc and
    Krusienski, Dean and Schultz, Tanja},
  year         = 2020,
  month        = oct,
  booktitle    = {Brain--Computer Interface Research},
  publisher    = {Springer},
  pages        = {47--54},
  doi          = {10.1007/978-3-030-49583-1_5},
  abstract     = {Brain-computer interfaces (BCIs) are envisioned to enable individuals with severe
    disabilities to regain the ability to communicate. Early BCIs have provided users with the
    ability to type messages one letter at a time, providing an important, but slow, means of
    communication for locked-in patients. However, natural speech contains substantially more
    information than a textual representation and can convey many important markers of human
    communication in addition to the sequence of words. A BCI that directly synthesizes speech from
    neural signals could harness this full expressive power of speech. In this study with
    motor-intact patients undergoing glioma removal, we demonstrate that high-quality audio signals
    can be synthesized from intracranial signals using a method from the speech synthesis community
    called Unit Selection. The Unit Selection approach concatenates speech units of the user to form
    new audio output and thereby produces natural speech in the user’s own voice.},