Authors:
Michael Wand
;
Matthias Janke
and
Tanja Schultz
Affiliation:
Karlsruhe Institute of Technology, Germany
Keyword(s):
EMG, EMG-based speech recognition, Silent speech interfaces, Phonetic decision tree.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Speech Recognition
;
Wearable Sensors and Systems
Abstract:
This study is concerned with the impact of speaking mode variabilities on speech recognition by surface
electromyography (EMG). In EMG-based speech recognition, we capture the electric potentials of the human
articulatory muscles by surface electrodes, so that the resulting signal can be used for speech processing. This
enables the user to communicate silently, without uttering any sound. Previous studies have shown that the
processing of silent speech creates a new challenge, namely that EMG signals of audible and silent speech
are quite distinct. In this study we consider EMG signals of three speaking modes: audibly spoken speech,
whispered speech, and silently mouthed speech. We present an approach to quantify the differences between
these speaking modes by means of phonetic decision trees and show that this measure correlates highly with
differences in the performance of a recognizer on the different speaking modes. We furthermore reinvestigate
the spectral mapping algorithm, whi
ch reduces the discrepancy between different speaking modes, and give
an evaluation of its effectiveness.
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