SESSION-INDEPENDENT EMG-BASED SPEECH RECOGNITION

Michael Wand, Tanja Schultz

Abstract

This paper reports on our recent research in speech recognition by surface electromyography (EMG), which is the technology of recording the electric activation potentials of the human articulatory muscles by surface electrodes in order to recognize speech. This method can be used to create Silent Speech Interfaces, since the EMG signal is available even when no audible signal is transmitted or captured. Several past studies have shown that EMG signals may vary greatly between different recording sessions, even of one and the same speaker. This paper shows that session-independent training methods may be used to obtain robust EMG-based speech recognizers which cope well with unseen recording sessions as well as with speaking mode variations. Our best session-independent recognition system, trained on 280 utterances of 7 different sessions, achieves an average 21.93% Word Error Rate (WER) on a testing vocabulary of 108 words. The overall best session-adaptive recognition system, based on a session-independent system and adapted towards the test session with 40 adaptation sentences, achieves an average WER of 15.66%, which is a relative improvement of 21% compared to the baseline average WER of 19.96% of a session-dependent recognition system trained only on a single session of 40 sentences.

References

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Paper Citation


in Harvard Style

Wand M. and Schultz T. (2011). SESSION-INDEPENDENT EMG-BASED SPEECH RECOGNITION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 295-300. DOI: 10.5220/0003169702950300


in Bibtex Style

@conference{biosignals11,
author={Michael Wand and Tanja Schultz},
title={SESSION-INDEPENDENT EMG-BASED SPEECH RECOGNITION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={295-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003169702950300},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - SESSION-INDEPENDENT EMG-BASED SPEECH RECOGNITION
SN - 978-989-8425-35-5
AU - Wand M.
AU - Schultz T.
PY - 2011
SP - 295
EP - 300
DO - 10.5220/0003169702950300