@inproceedings{botelho2020towards,
    title={Toward Silent Paralinguistics: Speech-to-EMG - Retrieving Articulatory Muscle Activity from Speech},
    author={Botelho, Catarina and Diener, Lorenz and Küster, Dennis and Scheck, Kevin and Amiriparian, Shahin and Schuller, Björn W. and Schultz, Tanja and Abad, Alberto and Trancoso, Isabel},
    booktitle={{INTERSPEECH} 2020 - 21st Annual Conference of the International Speech Communication Association},
    year={2020 (to appear)},
    url={https://www.csl.uni-bremen.de/cms/images/documents/publications/Interspeech2020_emg_to_speech_revised.pdf},
    abstract={Electromyographic (EMG) signals recorded during speech production encode information on articulatory muscle activity and also on the facial expression of emotion, thus representing a speech-related biosignal with strong potential for paralinguistic applications. In this work, we estimate the electrical activity of the muscles responsible for speech articulation directly from the speech signal. To this end, we first perform a neural conversion of speech features into electromyographic time domain features, and then attempt to retrieve the original EMG signal from the time domain features. We propose a feed forward neural network to address the first step of the problem (speech features to EMG features) and a neural network composed of a convolutional block and a bidirectional long short-term memory block to address the second problem (true EMG features to EMG signal). We observe that four out of the five originally proposed time domain features can be estimated reasonably well from the speech signal. Further,  the five time domain features are able to predict the original speech-related EMG signal with a concordance correlation coefficient of 0.663. We further compare our results with the ones achieved on the inverse problem of generating acoustic speech features from EMG features.},
}