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

Matthias Janke, Lorenz Diener
2017 IEEE/ACM Transactions on Audio Speech and Language Processing  
iii related EMG-to-speech work, shows a relative improvement of 29 % with our best performing mapping approach.  ...  Ein Vergleich der resultierenden Ergebnisse unseres besten Systems mit verwandten EMG-to-Speech Ansätzen zeigt eine relative Verbesserung von 29 %.  ...  We investigate the direct EMG-to-speech mapping based on the proposed artificial neural network types: feed-forward DNN and Long Short Term Memory networks.  ... 
doi:10.1109/taslp.2017.2738568 fatcat:qcct6eiqhrgarmbj7gh7cfcxmq

Neural Speaker Embeddings for Ultrasound-based Silent Speech Interfaces [article]

Amin Honarmandi Shandiz, László Tóth, Gábor Gosztolya, Alexandra Markó, Tamás Gábor Csapó
2021 arXiv   pre-print
Finally, we examined how the embedding vector influences the accuracy of our ultrasound-to-speech conversion network in a multi-speaker scenario.  ...  Articulatory-to-acoustic mapping seeks to reconstruct speech from a recording of the articulatory movements, for example, an ultrasound video.  ...  Wand et al. utilized domain-adversarial DNN training to increase the sessionindependency of their EMG-based speech recognizer [18] .  ... 
arXiv:2106.04552v2 fatcat:joiqe4xgrvg43eo4okppfrfpce

Silent Speech Interfaces for Speech Restoration: A Review

Jose A. Gonzalez-Lopez, Alejandro Gomez-Alanis, Juan M. Martin-Donas, Jose L. Perez-Cordoba, Angel M. Gomez
2020 IEEE Access  
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  ...  From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms.  ...  training [279] or the use of a domain-adversarial loss function to integrate session independence into neural network training [88] .  ... 
doi:10.1109/access.2020.3026579 fatcat:yvvaebeavfdfrav73sfs62a5dm

Silent Speech Interfaces for Speech Restoration: A Review [article]

Jose A. Gonzalez-Lopez, Alejandro Gomez-Alanis, Juan M. Martín-Doñas, José L. Pérez-Córdoba, Angel M. Gomez
2020 arXiv   pre-print
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  ...  From the biosignals, SSIs decode the intended message, using automatic speech recognition or speech synthesis algorithms.  ...  training [279] or the use of a domain-adversarial loss function to integrate session independence into neural network training [88] .  ... 
arXiv:2009.02110v2 fatcat:i2o4zxqko5anhn2eqivtnsd2di

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Angkoon Phinyomark, Erik Scheme
2018 Big Data and Cognitive Computing  
to handle "big data".  ...  This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets.  ...  Wand and colleagues [106, 107] also compared deep neural networks to commonly used machine learning approaches for EMG-based speech recognition, i.e., Gaussian mixture model (GMM), yielding accuracy  ... 
doi:10.3390/bdcc2030021 fatcat:h24h4mj6xvgdtgeg5xrmqre6nm

Final Program

2020 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)  
Convolutional Recurrent Neural Network Convolutional neural network Deep Learning Networks for Vowel Speech Imagery Convolutional neural networks Analysis of CNN Models to develop a New Appearance Model  ...  Therefore, this paper proposes two neural networks to classify vowels in speech imagery signals using SRP: a Convolutional Neural Network called sCNN and a Capsule Neural Network called sCapsNet.  ...  Proposal for a rotary micromotor structure based on CMOS-MEMS technology Griselda Stephany Abarca-Jiménez Design of position sensor of a linear micromotor based on CMOS-MEMS technology Guillermo  ... 
doi:10.1109/cce50788.2020.9299182 fatcat:dff7ylnwrzabdcv276gbwcgkji

Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

Rafael Anicet Zanini, Esther Luna Colombini
2020 Sensors  
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson's Disease (PD) electromyography  ...  (EMG) signals.  ...  the PD's EMG dataset used as the basis for our findings.  ... 
doi:10.3390/s20092605 pmid:32375217 fatcat:77omubgs4zedrjeguxgeq4a2vq

Hand-Gesture Recognition Based on EMG and Event-Based Camera Sensor Fusion: A Benchmark in Neuromorphic Computing

Enea Ceolini, Charlotte Frenkel, Sumit Bam Shrestha, Gemma Taverni, Lyes Khacef, Melika Payvand, Elisa Donati
2020 Frontiers in Neuroscience  
Hand gestures are a form of non-verbal communication used by individuals in conjunction with speech to communicate.  ...  According to the chip's constraints, we designed specific spiking neural networks (SNNs) for sensor fusion that showed classification accuracy comparable to the software baseline.  ...  Finally, we thank Garrick Orchard for supporting us with the use of the Loihi platform and the useful comments to the paper.  ... 
doi:10.3389/fnins.2020.00637 pmid:32903824 pmcid:PMC7438887 fatcat:jbelvtolezbw7kamovfl6pxiam

Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles

Zhile Yang, Kang Li, Qun Niu, Aoife Foley
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
model [#15449] Sangwook Kim, Jixiang Shen and Minho Lee 5:20PM Direct Conversion from Facial Myoelectric Signals to Speech using Deep Neural Networks [#15196] Lorenz Diener, Matthias Janke and Tanja  ...  Networks Using Softplus Units [#15271] Hao Zheng, Zhanlei Yang, Jizhong Liang, Yanpeng Li and Wenju Liu P394 Restoring High Frequency Spectral Envelopes Using Neural Networks for Speech Bandwidth Extension  ... 
doi:10.1109/ijcnn.2015.7280446 dblp:conf/ijcnn/YangLNF15 fatcat:6xlakikcfzfyhhm2spooe2j7ra

Emerging Natural User Interfaces in Mobile Computing: A Bottoms-Up Survey [article]

Kirill A. Shatilov, Dimitris Chatzopoulos, Lik-Hang Lee, Pan Hui
2019 arXiv   pre-print
Although there exist many ways of displaying information to mobile users, inputting data to a mobile device is, usually, limited to a conventional touch based interaction, that distracts users from their  ...  The concurrent development of bio-signal acquisition techniques and accompanying ecosystems offers a useful toolbox to address open challenges.  ...  CNN -Convolutional Neural Network; CLconvolutional layer; FCL -Fully Connected layer; RNN -Recurrent Neural Network; Fig. 14.  ... 
arXiv:1911.04794v2 fatcat:tuho2hdkmffkbhz3ew45t44sq4

Technical program

2020 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)  
Therefore, this paper proposes two neural networks to classify vowels in speech imagery signals using SRP: a Convolutional Neural Network called sCNN and a Capsule Neural Network called sCapsNet.  ...  Self-driving through a Time-distributed Convolutional Recurrent Neural Network Abstract: This paper proposes an approach based on the use of time dimension within a Convolutional and Recurrent Hybrid Neural  ...  Proposal for training a Cellular Neural Network using a Hybrid Artificial Bee Colony and Nelder-Mead Algorithms Abstract: In this work, we propose a methodology for training a Cellular Neural Network based  ... 
doi:10.1109/cce50788.2020.9299132 fatcat:xkw6xl7vvnan7enewajqfqwo4q

Deep Learning in Mining Biological Data

Mufti Mahmud, M. Shamim Kaiser, T. Martin McGinnity, Amir Hussain
2021 Cognitive Computation  
Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied  ...  Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures' applications to these data.  ...  Acknowledgements The authors would like to thank the members of the acslab (http://www.acsla b.info/) for valuable discussions. Author Contributions  ... 
doi:10.1007/s12559-020-09773-x pmid:33425045 pmcid:PMC7783296 fatcat:n4nk7gakfbb4fbhdi5pqeojwjm

Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Wei Li, Ping Shi, Hongliu Yu
2021 Frontiers in Neuroscience  
The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process.  ...  This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal  ...  Convolutional Neural NetworkConvolutional neural network (CNN) is the most extensive applications for DL architecture based on sEMG gesture recognition. proposed a user-adaptive CNN model, which is the  ... 
doi:10.3389/fnins.2021.621885 pmid:33981195 pmcid:PMC8107289 fatcat:m5iggijlmjc63momolexpwalbq

Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up Survey

Kirill A. Shatilov, Dimitris Chatzopoulos, Lik-Hang Lee, Pan Hui
2021 ACM transactions on interactive intelligent systems (TiiS)  
ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR.  ...  In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions.  ...  We also express our gratitude toward the participants of the survey (see Section 6), who helped us outline the degree of acceptance of novel ExG-based interfaces.  ... 
doi:10.1145/3457950 fatcat:nwoengjmn5efbke7sbgdjhq2vy

Design of large polyphase filters in the Quadratic Residue Number System

Gian Carlo Cardarilli, Alberto Nannarelli, Yann Oster, Massimo Petricca, Marco Re
2010 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers  
Systems II 8a2 -Speech Enhancement 8a3 -Topics in Speech & Audio 8a4 -Adaptive SP in Comm. 8a5 -Array-based Estimation Poster Sessions -TA8b 10:15-12:00 8b1-Coop. and Cognitive Transmission in Multi-Antenna  ...  Processing for Neural Signals TP5b Integrated Multimodal Sensing TP6a Computer Arithmetic II TP6b Computer Arithmetic III TP7a Microphone Array Processing for Speech Applications I TP7b Microphone Array  ... 
doi:10.1109/acssc.2010.5757589 fatcat:ccxnu5owr5fyrcjcqukumerueq
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