Towards excluding redundancy in electrode grid for automatic speech recognition based on surface EMG

Kubo, Takatomi; Yoshida, Masaki; Hattori, Takumu; Ikeda, Kazushi

doi:10.1016/j.neucom.2012.12.072

Cited by 14 publications

(3 citation statements)

References 18 publications

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“…Moreover, a large number of electrodes would lead to high computational complexity, overfitting of redundant data, and an increase in the system cost. The electrode optimization could help to greatly reduce the redundancy by selecting optimal positions where the activities of all different muscles could be recorded using a significantly reduced number of electrodes, which is also supported by the findings of related studies [16,29,30]. By employing electrode optimization, a system with a minimum number of electrodes would be easier to operate and more comfortable for the subjects, which may facilitate widespread applications of automatic speech recognition.…”

Section: Discussionmentioning

confidence: 72%

“…Towards addressing the limitations of acoustic signals, surface electromyography (sEMG) that consists of electrophysiology information of muscles associated with speaking has been considered as an alternative input for automatic speech recognition [13][14][15][16]. Compared with the acoustic signals, the sEMG signals would not be affected by interferences from the acoustic ambient noises, making it possible to achieve accurate recognition of human speech even in noisy environments.…”

Section: Introductionmentioning

confidence: 99%

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Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography

et al. 2021

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Objective. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes. Approach. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for SSR could be systemically explored. A total of 120 closely spaced electrodes were placed on the facial and neck muscles to collect the high-density sEMG signals for classifying ten digits (0–9) silently spoken in both English and Chinese. The sequential forward selection algorithm was adopted to explore the optimal electrodes configurations. Main Results. The results showed that the classification accuracy increased rapidly and became saturated quickly when the number of selected electrodes increased from 1 to 120. Using only ten optimal electrodes could achieve a classification accuracy of 86% for English and 94% for Chinese, whereas as many as 40 non-optimized electrodes were required to obtain comparable accuracies. Also, the optimally selected electrodes seemed to be mostly distributed on the neck instead of the facial region, and more electrodes were required for English recognition to achieve the same accuracy. Significance. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography

et al. 2021

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“…Speaking different words or languages requires different ways of pronunciation and therefore involves different muscular contraction patterns, which could be recorded by a non-invasive technique called surface electromyography (sEMG) via placing EMG sensors on the skin surface for measuring the corresponding electrical signals. Since the sEMG signals contain substantial dynamic information about the articulatory muscle activities, the sEMG sensors could be used in automatic speech recognition (ASR) systems that convert the electrical sEMG signals associated with human speaking into computer-readable textual messages [1]. Unlike conventional recognition methods using the human voice collecting from acoustic sensors, the sEMG-based ASR systems do not rely on any acoustic signals, that are not always available and easily contaminated by various environmental noises.…”

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confidence: 99%

Automatic Speech Recognition in Different Languages Using High-Density Surface Electromyography Sensors

et al. 2021

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Comparison of feature evaluation criteria for speech recognition based on electromyography

Srisuwan

Phukpattaranont

Limsakul

2017

Med Biol Eng Comput

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In this paper, we present a performance comparison of 14 feature evaluation criteria and 4 classifiers for isolated Thai word classification based on electromyography signals (EMG) to find a near-optimal criterion and classifier. Ten subjects spoke 11 Thai number words in both audible and silent modes while the EMG signal from five positions of the facial and neck muscles were captured. After signal collection and preprocessing, 22 EMG features widely used in the EMG recognition field were computed and were then evaluated based on 14 evaluation criteria including both independent criteria (IC) and dependent criteria (DC) for feature evaluation and selection. Subsequently, the top nine features were selected for each criterion, and were used as inputs to classifiers. Four types of classifier were employed with 10-fold cross-validation to estimate classification performance. The results showed that features selected with a DC on a Fisher's least square linear discriminant classifier (D_FLDA) used with a linear Bayes normal classifier (LBN) gave the best average accuracies, of 93.25 and 80.12% in the audible and the silent modes, respectively.

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Towards excluding redundancy in electrode grid for automatic speech recognition based on surface EMG

Cited by 14 publications

References 18 publications

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography

Automatic Speech Recognition in Different Languages Using High-Density Surface Electromyography Sensors

Comparison of feature evaluation criteria for speech recognition based on electromyography

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