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Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces
2016
PLoS Computational Biology
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Real-Time Articulatory-Based Speech Synthesis for BCIs PLOS Computational Biology | Fig 1. Articulatory-based speech synthesizer. ...
The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced ...
Acknowledgments The authors wish to thank Silvain Gerber for his help in the statistical analysis of the results.
Author Contributions Conceived and designed the experiments: FB TH LG BY. ...
doi:10.1371/journal.pcbi.1005119
pmid:27880768
pmcid:PMC5120792
fatcat:5zg6yqunvfda7aclv5th7rfbmu
Key considerations in designing a speech brain-computer interface
2016
Journal of Physiology - Paris
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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. ...
The authors also wish to thank Marie-Pierre Gilotin and Manuela Oddoux for clinical help during awake surgery and the patient who participated in the study. ...
The special case of articulatory-based speech synthesis The use of an articulatory speech synthesizer can be of particular interest for a BCI application for several reasons. ...
doi:10.1016/j.jphysparis.2017.07.002
pmid:28756027
fatcat:5jdkvghmgfczvbjybvhroamnmy
Key Considerations In Designing A Speech Brain-Computer Interface
2018
Zenodo
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Bocquelet F, Hueber T, Girin L, Chabardès S, Yvert B (2016) Key considerations in designing a speech brain computer interface. J Physiol Paris, 110: 392-401 ...
The authors also wish to thank Marie-Pierre Gilotin and Manuela Oddoux for clinical help during awake surgery and the patient who participated in the study. ...
The special case of articulatory-based speech synthesis The use of an articulatory speech synthesizer can be of particular interest for a BCI application for several reasons. ...
doi:10.5281/zenodo.1242931
fatcat:ztdpzu4g7zfvtnehqfhasvp7nu
Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings
[article]
2019
arXiv
pre-print
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To do this, we impose auxiliary losses on latent representations for articulatory movements, speech acoustics and session specific non-linearities. ...
In this study, we propose a novel deep network architecture Brain2Char, for directly decoding text (specifically character sequences) from direct brain recordings (called Electrocorticography, ECoG). ...
Pasley In speech processing applications like automatic speech recognition (ASR) and text-to-speech synthesis (TTS), much progress has been made to achieve near-human performance on standard benchmarks ...
arXiv:1909.01401v1
fatcat:mmmj75x7v5dhdk2jfgfqnzpm3m
The Potential for a Speech Brain–Computer Interface Using Chronic Electrocorticography
2019
Neurotherapeutics
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This review discusses and outlines the current state-of-the-art for speech BCI and explores what a speech BCI using chronic ECoG might entail. ...
A BCI for speech would enable communication in real time via neural correlates of attempted or imagined speech. ...
deep neural network to map articulations to their corresponding acoustic outputs. ...
doi:10.1007/s13311-018-00692-2
pmid:30617653
pmcid:PMC6361062
fatcat:6y66u77cdreb7jhku666wklyha
Silent Speech Interfaces for Speech Restoration: A Review
2020
IEEE Access
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INDEX TERMS Silent speech interface, speech restoration, automatic speech recognition, speech synthesis, deep neural networks, brain computer interfaces, speech and language disorders, voice disorders, ...
From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. ...
For direct speech synthesis, various neural network architectures have been investigated, including feed-forward neural networks [27] , [99] , [101] , convolutional neural networks (CNNs) [170] - ...
doi:10.1109/access.2020.3026579
fatcat:yvvaebeavfdfrav73sfs62a5dm
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning
[article]
2019
arXiv
pre-print
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signal responsible for natural speech synthesis. ...
In order to infer imagined speech from active thoughts, we propose a novel hierarchical deep learning BCI system for subject-independent classification of 11 speech tokens including phonemes and words. ...
Yet, there is hardly any work investigating the applicability and performance of such deep learning techniques for speech imagery-based BCI. ...
arXiv:1904.05746v1
fatcat:6gqhqy3yyrefpiyjjyevw22l6q
Silent Speech Interfaces for Speech Restoration: A Review
[article]
2020
arXiv
pre-print
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From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms. ...
tracking of articulator movements using imaging techniques. ...
For direct speech synthesis, various neural network architectures have been investigated, including feed-forward neural networks [27] , [99] , [101] , convolutional neural networks (CNNs) [170] - ...
arXiv:2009.02110v2
fatcat:i2o4zxqko5anhn2eqivtnsd2di
SPEAK YOUR MIND! Towards Imagined Speech Recognition with Hierarchical Deep Learning
2019
Interspeech 2019
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signal responsible for natural speech synthesis. ...
In order to infer imagined speech from active thoughts, we propose a novel hierarchical deep learning BCI system for subject-independent classification of 11 speech tokens including phonemes and words. ...
Yet, there is hardly any work investigating the applicability and performance of such deep learning techniques for speech imagery-based BCI. ...
doi:10.21437/interspeech.2019-3041
dblp:conf/interspeech/SahaAF19
fatcat:jvo6xsnwjjc2rct5xsrvc2p4cy
Intelligible speech synthesis from neural decoding of spoken sentences
[article]
2018
bioRxiv
pre-print
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A recurrent neural network first decoded vocal tract physiological signals from direct cortical recordings, and then transformed them to acoustic speech output. ...
Additionally, speech decoding was not only effective for audibly produced speech, but also when participants silently mimed speech. ...
For this purpose, we used an existing annotated speech 392 database (Wall Street Journal Corpus) 49 and trained speaker independent deep recurrent 393 network regression models to predict these place-manner ...
doi:10.1101/481267
fatcat:hed472oeyvgxjl5a6rhsy3klwu
Speech synthesis from neural decoding of spoken sentences
2019
Nature
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Recurrent neural networks first decoded directly recorded cortical activity into representations of articulatory movement, and then transformed these representations into speech acoustics. ...
Technology that translates neural activity into speech would be transformative for people who are unable to communicate as a result of neurological impairments. ...
Moses for comments on the manuscript and B. Speidel for his help reconstructing MRI images. This work was supported by grants from the NIH (DP2 OD008627 and U01 NS098971-01). ...
doi:10.1038/s41586-019-1119-1
pmid:31019317
fatcat:7taeckhko5fhnbk4gwio4y2ogy
Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review
2021
Sensors
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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 ...
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. ...
Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s21041399
pmid:33671282
fatcat:je4cmqkulnbmpbji3owxgr7f24
Decoding Imagined and Spoken Phrases From Non-invasive Neural (MEG) Signals
2020
Frontiers in Neuroscience
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Two machine learning algorithms were used. One was an artificial neural network (ANN) with statistical features as the baseline approach. ...
Direct decoding of imagined speech from the neural signals (and then driving a speech synthesizer) has the potential for a higher communication rate. ...
Hernandez-Mulero and Saleem Malik for their help on the data collection at Cook Children's Hospital, Fort Worth, TX. We also thank Dr. Ted Mau, Dr. Myungjong Kim, Dr. Mark McManis, Dr. ...
doi:10.3389/fnins.2020.00290
pmid:32317917
pmcid:PMC7154084
fatcat:o7exd5plyjhfnmitlt7qnotf34
A survey of deep neural network architectures and their applications
2017
Neurocomputing
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In this paper, we discuss some widely-used deep learning architectures and their practical applications. ...
Deep learning approaches have also been found to be suitable for big data analysis with successful applications to computer vision, pattern recognition, speech recognition, natural language processing, ...
Additionally, deep learning techniques can also be used for head motion synthesis [37] and speech enhancement [81] . ...
doi:10.1016/j.neucom.2016.12.038
fatcat:nkxvbhp47rfflpi5jev7hk4yq4
Decoding spoken English phonemes from intracortical electrode arrays in dorsal precentral gyrus
[article]
2020
bioRxiv
pre-print
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direction for speech BCIs. ...
basis set for speech: 39 English phonemes. ...
We also thank Professor Mark Slutsky for providing the many words list; our Stanford NPTL and NPSL group for helpful discussions; Beverly Davis, Erika Siauciunas, and Nancy Lam for administrative support ...
doi:10.1101/2020.06.30.180935
fatcat:ebx5dfa62je2basdnuhv245b6i
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