@inproceedings{diener2015direct,
  title        = {Direct Conversion from Facial Myoelectric Signals to Speech using Deep Neural
    Networks},
  author       = {Diener, Lorenz and Janke, Matthias and Schultz, Tanja},
  year         = 2015,
  month        = oct,
  booktitle    = {{IJCNN} 2015 - 2015 International Joint Conference on Neural Networks},
  pages        = {1--7},
  doi          = {10.1109/IJCNN.2015.7280404},
  abstract     = {This paper presents our first results using Deep Neural Networks for surface
    electromyographic (EMG) speech synthesis. The proposed approach enables a direct mapping from
    EMG signals captured from the articulatory muscle movements to the acoustic speech signal.
    Features are processed from multiple EMG channels and are fed into a feed forward neural network
    to achieve a mapping to the target acoustic speech output. We show that this approach is
    feasible to generate speech output from the input EMG signal and compare the results to a prior
    mapping technique based on Gaussian mixture models. The comparison is conducted via objective
    Mel-Cepstral distortion scores and subjective listening test evaluations. It shows that the
    proposed Deep Neural Network approach gives substantial improvements for both evaluation
    criteria.},
  keywords     = {electromyography, silent speech interface, deep neural networks},
  url          = {https://halcy.de/cites/pdf/diener2015direct.pdf},
}