@mastersthesis{diener2015improving,
  title        = {Improving Unit Selection based EMG-to-Speech Conversion},
  author       = {Diener, Lorenz},
  year         = 2015,
  month        = jul,
  school       = {Karlsruher Institut für Technologie},
  supervisor   = {Janke, Matthias and Schultz, Tanja},
  abstract     = {This master’s thesis introduces a new approach to improve the unit-selection based
    conversion of facial myoelectric signals to audible speech. Surface electromyography is the
    recording of electric signals generated by muscle activity using surface electrodes attached to
    the skin. Past work has shown that it is feasible to generate audible speech signals from facial
    electromyographic activity generated during speech production, using several different
    approaches. This work focuses on the unit-selection approach to conversion, where the speech
    signal is reconstructed by concatenating pieces of target audio data selected by a similarity
    criterion calculated on the parallel sequence of source electromyographic data. A novel
    approach, based on optimizing the database that units are selected from by using unit clustering
    to generate more prototypical units and improve the selection process, is introduced and
    evaluated. In total, we obtain a qualitative improvement of up to 14.92 percent relative over a
    baseline unit selection system, while improving the time taken for conversion by up to 98%.},
  url          = {https://halcy.de/cites/pdf/diener2015improving.pdf},
}