358 research outputs found

    EMG-to-Speech: Direct Generation of Speech from Facial Electromyographic Signals

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    The general objective of this work is the design, implementation, improvement and evaluation of a system that uses surface electromyographic (EMG) signals and directly synthesizes an audible speech output: EMG-to-speech

    Velum movement detection based on surface electromyography for speech interface

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    Conventional speech communication systems do not perform well in the absence of an intelligible acoustic signal. Silent Speech Interfaces enable speech communication to take place with speech-handicapped users and in noisy environments. However, since no acoustic signal is available, information on nasality may be absent, which is an important and relevant characteristic of several languages, particularly European Portuguese. In this paper we propose a non-invasive method - surface Electromyography (EMG) electrodes - positioned in the face and neck regions to explore the existence of useful information about the velum movement. The applied procedure takes advantage of Real-Time Magnetic Resonance Imaging (RT-MRI) data, collected from the same speakers, to interpret and validate EMG data. By ensuring compatible scenario conditions and proper alignment between the EMG and RT-MRI data, we are able to estimate when the velum moves and the probable type of movement under a nasality occurrence. Overall results of this experiment revealed interesting and distinct characteristics in the EMG signal when a nasal vowel is uttered and that it is possible to detect velum movement, particularly by sensors positioned below the ear between the mastoid process and the mandible in the upper neck region.info:eu-repo/semantics/publishedVersio

    Brain-Computer Interfaces for Speech Communication

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    This paper briefly reviews current silent speech methodologies for normal and disabled individuals. Current techniques utilizing electromyographic (EMG) recordings of vocal tract movements are useful for physically healthy individuals but fail for tetraplegic individuals who do not have accurate voluntary control over the speech articulators. Alternative methods utilizing EMG from other body parts (e.g., hand, arm, or facial muscles) or electroencephalography (EEG) can provide capable silent communication to severely paralyzed users, though current interfaces are extremely slow relative to normal conversation rates and require constant attention to a computer screen that provides visual feedback and/or cueing. We present a novel approach to the problem of silent speech via an intracortical microelectrode brain computer interface (BCI) to predict intended speech information directly from the activity of neurons involved in speech production. The predicted speech is synthesized and acoustically fed back to the user with a delay under 50 ms. We demonstrate that the Neurotrophic Electrode used in the BCI is capable of providing useful neural recordings for over 4 years, a necessary property for BCIs that need to remain viable over the lifespan of the user. Other design considerations include neural decoding techniques based on previous research involving BCIs for computer cursor or robotic arm control via prediction of intended movement kinematics from motor cortical signals in monkeys and humans. Initial results from a study of continuous speech production with instantaneous acoustic feedback show the BCI user was able to improve his control over an artificial speech synthesizer both within and across recording sessions. The success of this initial trial validates the potential of the intracortical microelectrode-based approach for providing a speech prosthesis that can allow much more rapid communication rates

    Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

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    Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this book is substantially improved in terms of accuracy, flexibility, and robustness

    Advancing Electromyographic Continuous Speech Recognition: Signal Preprocessing and Modeling

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    Speech is the natural medium of human communication, but audible speech can be overheard by bystanders and excludes speech-disabled people. This work presents a speech recognizer based on surface electromyography, where electric potentials of the facial muscles are captured by surface electrodes, allowing speech to be processed nonacoustically. A system which was state-of-the-art at the beginning of this book is substantially improved in terms of accuracy, flexibility, and robustness

    Current state of digital signal processing in myoelectric interfaces and related applications

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    This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.Peer reviewe

    Silent Speech Interfaces for Speech Restoration: A Review

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    This work was supported in part by the Agencia Estatal de Investigacion (AEI) under Grant PID2019-108040RB-C22/AEI/10.13039/501100011033. The work of Jose A. Gonzalez-Lopez was supported in part by the Spanish Ministry of Science, Innovation and Universities under Juan de la Cierva-Incorporation Fellowship (IJCI-2017-32926).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.Agencia Estatal de Investigacion (AEI) PID2019-108040RB-C22/AEI/10.13039/501100011033Spanish Ministry of Science, Innovation and Universities under Juan de la Cierva-Incorporation Fellowship IJCI-2017-3292
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