Speech is our most natural and efficient way of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy in a public place), or health conditions (e.g., laryngectomy), hindering the consideration of audible speech. In this regard, silent speech interfaces (SSI) have been proposed (e.g., considering video, electromyography), however, many technologies still face limitations regarding their everyday use, e.g., the need to place equipment in contact with the speaker (e.g., electrodes/ultrasound probe), and raise technical (e.g., lighting conditions for video) or privacy concerns. In this context, the consideration of technologies that can help tackle these issues, e.g, by being contactless and/or placed in the environment, can foster the widespread use of SSI. In this article, continuous-wave radar is explored to assess its potential for SSI. To this end, a corpus of 13 words was acquired, for 3 speakers, and different classifiers were tested on the resulting data. The best results, obtained using Bagging classifier, trained for each speaker, with 5-fold cross-validation, yielded an average accuracy of 0.826, an encouraging result that establishes promising grounds for further exploration of this technology for silent speech recognition.
Cite as: Ferreira, D., Silva, S., Curado, F., Teixeira, A. (2021) RaSSpeR: Radar-Based Silent Speech Recognition. Proc. Interspeech 2021, 646-650, doi: 10.21437/Interspeech.2021-1413
@inproceedings{ferreira21_interspeech,
author={David Ferreira and Samuel Silva and Francisco Curado and António Teixeira},
title={{RaSSpeR: Radar-Based Silent Speech Recognition}},
year=2021,
booktitle={Proc. Interspeech 2021},
pages={646--650},
doi={10.21437/Interspeech.2021-1413}
}