Compensation of recording position shifts for a myoelectric Silent Speech Recognizer

Wand, Michael; Schulte, Christopher M.; Janke, Matthias; Schultz, Tanja

doi:10.1109/icassp.2014.6853968

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Both gestures seem to lead to a distinct zone of inactivity along the y-axis. We didn't validate if the found path is actually consistent with the anatomical Gestures WS CS 11,17,21,23,24 98.8 94. 6 1,2,3,11,17,18,21,23,24,25 97.0 92.…”

Section: Calibration Gesturementioning

confidence: 68%

See 1 more Smart Citation

Advancing Muscle-Computer Interfaces with High-Density Electromyography

Amma

Krings

Böer

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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144

102

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In this paper we present our results on using electromyographic (EMG) sensor arrays for finger gesture recognition. Sensing muscle activity allows to capture finger motion without placing sensors directly at the hand or fingers and thus may be used to build unobtrusive body-worn interfaces. We use an electrode array with 192 electrodes to record a highdensity EMG of the upper forearm muscles. We present in detail a baseline system for gesture recognition on our dataset, using a naive Bayes classifier to discriminate the 27 gestures. We recorded 25 sessions from 5 subjects. We report an average accuracy of 90% for the within-session scenario, showing the feasibility of the EMG approach to discriminate a large number of subtle gestures. We analyze the effect of the number of used electrodes on the recognition performance and show the benefit of using high numbers of electrodes. Crosssession recognition typically suffers from electrode position changes from session to session. We present two methods to estimate the electrode shift between sessions based on a small amount of calibration data and compare it to a baseline system with no shift compensation. The presented methods raise the accuracy from 59% baseline accuracy to 75% accuracy after shift compensation. The dataset is publicly available.

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Section: Calibration Gesturementioning

confidence: 68%

“…Wand et. al [21] use EMG arrays attached to the face for silentspeech recognition. They analyze cross-session performance and successfully apply shift compensation by correlating all possible shifts within certain boundary conditions and choosing the shift with highest correlation.…”

Section: Related Workmentioning

confidence: 99%

Advancing Muscle-Computer Interfaces with High-Density Electromyography

Amma

Krings

Böer

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Self Cite

144

102

View full text Add to dashboard Cite

show abstract

“…While initially, a small amount of training data may be preferable, collecting a larger amount of training data may be a good way to increase system robustness. Other possible solutions that we will explore in the future are signal level adaptation [19] and initializing the real-time system with sessionindependent models [20] to use the incoming training data for unsupervised adaptation during run-time.…”

Section: Discussionmentioning

confidence: 99%

Investigating Objective Intelligibility in Real-Time EMG-to-Speech Conversion

Diener

Schultz

2018

Interspeech 2018

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This paper presents an analysis of the influence of various system parameters on the output quality of our neural network based real-time EMG-to-Speech conversion system. This EMG-to-Speech system allows for the direct conversion of facial surface electromyographic signals into audible speech in real time, allowing for a closed-loop setup where users get direct audio feedback. Such a setup opens new avenues for research and applications through co-adaptation approaches. In this paper, we evaluate the influence of several parameters on the output quality, such as time context, EMG-Audio delay, network-, training data-and Mel spectrogram size. The resulting output quality is evaluated based on the objective output quality measure STOI.

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An initial investigation into the real-time conversion of facial surface EMG signals to audible speech

Diener

Janke

Schultz

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper presents early-stage results of our investigations into the direct conversion of facial surface electromyographic (EMG) signals into audible speech in a real-time setting, enabling novel avenues for research and system improvement through real-time feedback. The system uses a pipeline approach to enable online acquisition of EMG data, extraction of EMG features, mapping of EMG features to audio features, synthesis of audio waveforms from audio features and output of the audio waveforms via speakers or headphones. Our system allows for performing EMG-to-Speech conversion with low latency and on a continuous stream of EMG data, enabling near instantaneous audio output during audible as well as silent speech production. In this paper, we present an analysis of our systems components for latency incurred, as well as the tradeoffs between conversion quality, latency and training duration required.

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Compensation of recording position shifts for a myoelectric Silent Speech Recognizer

Cited by 5 publications

References 18 publications

Advancing Muscle-Computer Interfaces with High-Density Electromyography

Advancing Muscle-Computer Interfaces with High-Density Electromyography

Investigating Objective Intelligibility in Real-Time EMG-to-Speech Conversion

An initial investigation into the real-time conversion of facial surface EMG signals to audible speech

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