Automatic Speech Recognition Based on Electromyographic Biosignals

Jou, Szu-Chen Stan; Schultz, Tanja

doi:10.1007/978-3-540-92219-3_23

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…When considering a method that provides artificial communication to a speech-deprived individual, one must identify the most efficient means given the nature of the individual’s impairment. Some methods rely on non-vocalized articulator movements or other sub-vocalizations (Betts and Jorgensen, 2006; Fagan et al, 2008; Jorgensen et al, 2003; Jou et al, 2006; Jou and Schultz, 2009; Maier-Hein et al, 2005; Mendes et al, 2008; Walliczek et al, 2006; Wand and Schultz, 2009) which can be helpful for speech deprived individuals (e.g. laryngectomy patients).…”

Section: Section 1: Introductionmentioning

confidence: 99%

“…Current methods for silent speech available to neurologically normal individuals or those with damaged vocal tracts typically involve the placement of surface electromyographic (EMG) electrodes on the orofacial and laryngeal speech articulators (Betts and Jorgensen, 2006; Fagan et al, 2008; Jorgensen et al, 2003; Jou et al, 2006; Jou and Schultz, 2009; Maier-Hein et al, 2005; Mendes et al, 2008; Wand and Schultz, 2009). Electrical recordings are transmitted from the electrodes to an analysis computer which has been trained to recognize a small vocabulary of words based upon the speaker’s EMG pattern.…”

Section: Section 1: Introductionmentioning

confidence: 99%

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Brain–computer interfaces for speech communication

Brumberg

Nieto-Castañón²,

Kennedy

et al. 2010

Speech Communication

215

138

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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.© 2010 Elsevier B.V. All rights reserved. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. (Betts and Jorgensen, 2006;Fagan et al., 2008;Jorgensen et al., 2003;Jou et al., 2006;Jou and Schultz, 2009;Maier-Hein et al., 2005;Mendes et al., 2008;Walliczek et al., 2006;Wand and Schultz, 2009) which can be helpful for speech deprived individuals (e.g. laryngectomy patients). Unfortunately, these options are not feasible for profoundly paralyzed individuals such as those suffering from locked-in syndrome, which is characterized by near-complete paralysis. Locked-in patients often retain slow eye movement or eye blink control which can be used to answer simple yes/ n...

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Section: Section 1: Introductionmentioning

confidence: 99%

Section: Section 1: Introductionmentioning

confidence: 99%

Brain–computer interfaces for speech communication

Brumberg

Nieto-Castañón²,

Kennedy

et al. 2010

Speech Communication

215

138

View full text Add to dashboard Cite

show abstract

“…This TD feature set was originally proposed by [28], [29] and has been used to convert speech-related EMG to acoustic speech in several works, such as [9], [30], and [7].…”

Section: Feature Extractionmentioning

confidence: 99%

Toward Silent Paralinguistics: Speech-to-EMG — Retrieving Articulatory Muscle Activity from Speech

Botelho¹,

Diener²,

Küster³

et al. 2020

Interspeech 2020

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Electromyographic (EMG) signals recorded during speech production encode information on articulatory muscle activity and also on the facial expression of emotion, thus representing a speech-related biosignal with strong potential for paralinguistic applications. In this work, we estimate the electrical activity of the muscles responsible for speech articulation directly from the speech signal. To this end, we first perform a neural conversion of speech features into electromyographic time domain features, and then attempt to retrieve the original EMG signal from the time domain features. We propose a feed forward neural network to address the first step of the problem (speech features to EMG features) and a neural network composed of a convolutional block and a bidirectional long short-term memory block to address the second problem (true EMG features to EMG signal). We observe that four out of the five originally proposed time domain features can be estimated reasonably well from the speech signal. Further, the five time domain features are able to predict the original speech-related EMG signal with a concordance correlation coefficient of 0.663. We further compare our results with the ones achieved on the inverse problem of generating acoustic speech features from EMG features.

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“…Kübler et al [43] reported a strong correlation between physical impairment and BCI performance (Figure 7). Some methods rely on non-vocalized articulator movements or other sub-vocalizations [40][41] [42] which can be helpful for speech deprived individuals (e.g. laryngectomy patients).…”

Section: Bci Applicationsmentioning

confidence: 99%

Brain signal and image analysis using machine learning methods

Kanas¹,

Κανάς²

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Η δουλειά η οποία παρουσιάζεται σε αυτήν την διδακτορική διατριβή ανήκει στο πλαίσιο της μηχανικής μάθησης και την ανάπτυξη μεθοδολογιών για την επεξεργασία και ανάλυση μονοδιάστατων και διδιάστατων εγκεφαλικών σημάτων. Πιο συγκεκριμένα, εστιάζεται: 1) στην μελέτη, επεξεργασία και ανάλυση εγκεφαλικών σημάτων ηλεκτροκορτικογραφήματος για τον εντοπισμό φωνητικής δραστηριοποίησης και την ταξινόμηση συλλαβών με σκοπό τον σχεδιασμό και ανάπτυξη ενός BCI συστήματος για την αποκατάσταση ασθενών με προβλήματα ομιλίας, 2) στην επεξεργασία εικόνων μαγνητικής τομογραφίας εγκεφάλου με σκοπό την τμηματοποίηση και ταξινόμηση καρκινικών εγκεφαλικών όγκων, και 3) την μαθηματική μοντελοποίηση δικτύων βιολογικών νευρώνων.

show abstract

Automatic Speech Recognition Based on Electromyographic Biosignals

Cited by 4 publications

References 15 publications

Brain–computer interfaces for speech communication

Brain–computer interfaces for speech communication

Toward Silent Paralinguistics: Speech-to-EMG — Retrieving Articulatory Muscle Activity from Speech

Brain signal and image analysis using machine learning methods

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