The Effects of Channel Number on Classification Performance for sEMG-based Speech Recognition

Wang, Xiaochen; Zhu, Mingxing; Cui, H.; Yang, Zijian; Wang, Xin; Zhang, Haoshi; Wang, Cheng; Deng, Hanjie; Chen, Shixiong; Li, Guanglin

doi:10.1109/embc44109.2020.9176260

“…In [234], transfer learning was found to be beneficial for silent speech recognition from EMG signals by exploiting neural networks trained on a image classification task as powerful feature extraction models. More recently, in [235], an empirical study was conducted to investigate the effect of the number of sEMG channels in silent speech recognition.…”

Section: B Muscle Activitymentioning

confidence: 99%

Silent Speech Interfaces for Speech Restoration: A Review

Gonzalez-Lopez,

Gomez-Alanis,

Martín-Doñas

et al. 2020

Preprint

0

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This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.

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Silent Speech Interfaces for Speech Restoration: A Review

Gonzalez-Lopez

¹

,

Gomez-Alanis

²

,

Donas

³

et al. 2020

View full text Add to dashboard Cite

This review summarises the status of silent speech interface (SSI) research. SSIs rely on non-acoustic biosignals generated by the human body during speech production to enable communication whenever normal verbal communication is not possible or not desirable. In this review, we focus on the first case and present latest SSI research aimed at providing new alternative and augmentative communication methods for persons with severe speech disorders. SSIs can employ a variety of biosignals to enable silent communication, such as electrophysiological recordings of neural activity, electromyographic (EMG) recordings of vocal tract movements or the direct tracking of articulator movements using imaging techniques. Depending on the disorder, some sensing techniques may be better suited than others to capture speech-related information. For instance, EMG and imaging techniques are well suited for laryngectomised patients, whose vocal tract remains almost intact but are unable to speak after the removal of the vocal folds, but fail for severely paralysed individuals. From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. Despite considerable advances in recent years, most present-day SSIs have only been validated in laboratory settings for healthy users. Thus, as discussed in this paper, a number of challenges remain to be addressed in future research before SSIs can be promoted to real-world applications. If these issues can be addressed successfully, future SSIs will improve the lives of persons with severe speech impairments by restoring their communication capabilities.

show abstract