A Deep Neural Network-Based Permanent Magnet Localization for Tongue Tracking

Sebkhi, Nordine; Sahadat, Nazmus; Hersek, Sinan; Bhavsar, Arpan; Siahpoushan, Shayan; Ghoovanloo, Maysam

doi:10.1109/jsen.2019.2923585

Cited by 34 publications

(9 citation statements)

References 26 publications

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“…Articulation is the ability to physically move the tongue, lips, teeth, and chin to produce a series of voice sounds that make up words and sentences. So an articulation-to-speech(ATS) synthesis is underway to restore the original voices of patients who produce abnormal languages, such as speech disorders [ 101 , 102 , 103 , 104 ]. Because this technique maps articulatory information directly to speech [ 104 ], it is possible to generate voice from articulatory movement data without subject making any sound [ 103 ].…”

Section: Deep Learning Based Voice Recognitionmentioning

confidence: 99%

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Lee

Seong

Ozlu

et al. 2021

Sensors

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Voice is one of the essential mechanisms for communicating and expressing one’s intentions as a human being. There are several causes of voice inability, including disease, accident, vocal abuse, medical surgery, ageing, and environmental pollution, and the risk of voice loss continues to increase. Novel approaches should have been developed for speech recognition and production because that would seriously undermine the quality of life and sometimes leads to isolation from society. In this review, we survey mouth interface technologies which are mouth-mounted devices for speech recognition, production, and volitional control, and the corresponding research to develop artificial mouth technologies based on various sensors, including electromyography (EMG), electroencephalography (EEG), electropalatography (EPG), electromagnetic articulography (EMA), permanent magnet articulography (PMA), gyros, images and 3-axial magnetic sensors, especially with deep learning techniques. We especially research various deep learning technologies related to voice recognition, including visual speech recognition, silent speech interface, and analyze its flow, and systematize them into a taxonomy. Finally, we discuss methods to solve the communication problems of people with disabilities in speaking and future research with respect to deep learning components.

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Section: Deep Learning Based Voice Recognitionmentioning

confidence: 99%

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Lee

Seong

Ozlu

et al. 2021

Sensors

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“…Nevertheless, the data thus acquired are a composite of all the magnetic fields generated by the magnets, and so their relation with the spatial position of the magnetic tracers is less explicit in PMA. In consequence, additional pre-processing is needed [247].…”

Section: ) Magnetic Articulographymentioning

confidence: 99%

Silent Speech Interfaces for Speech Restoration: A Review

et al. 2020

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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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“…This useful property, together with improved computing methods and hardware for training them, have made neural networks useful for prediction in a variety of tasks in recent years. There has even been some preliminary studies applying neural nets to permanent magnet localization [51].…”

Section: Lstm Based Localizationmentioning

confidence: 99%

Permanent Magnet-Based Localization for Growing Robots in Medical Applications

Watson

Morimoto

2020

IEEE Robot. Autom. Lett.

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Additionally I would like thank the other members of my committee. In particular, I would like to thank Professor Tolley for having me as a reader for a course he taught on robotics which both helped to support my education as well as further my understanding of the subject. I would also like to thank Professor Yip, whose reinforcement learning course has inspired me to consider new research possibilities for the future.Further, I would like to thank Dr. Cedric Girerd for his help and advice with regards to this work, as well as his perspective on research in general. Finally, I would also like to thank the other members of the Moromoto Lab for their encouragement and support along the way. This thesis, in part, has been submitted for publication of the material as it may appear in

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A Deep Neural Network-Based Permanent Magnet Localization for Tongue Tracking

Cited by 34 publications

References 26 publications

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review

Silent Speech Interfaces for Speech Restoration: A Review

Permanent Magnet-Based Localization for Growing Robots in Medical Applications

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