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1
Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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2
Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.archives-ouvertes.fr/hal-03578503 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapour, Singapore (2022)
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3
Estimating Vocal Tract Resonances of Synthesized High-Pitched Vowels Using CNN ...
Mikusova, Ivana. - : TU Wien, 2022
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4
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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5
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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6
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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7
Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal
In: Sensors; Volume 22; Issue 8; Pages: 3070 (2022)
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8
ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features
In: Sensors; Volume 22; Issue 4; Pages: 1660 (2022)
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9
Automatic Classification Framework of Tongue Feature Based on Convolutional Neural Networks
In: Micromachines; Volume 13; Issue 4; Pages: 501 (2022)
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10
Short Text Aspect-Based Sentiment Analysis Based on CNN + BiGRU
In: Applied Sciences; Volume 12; Issue 5; Pages: 2707 (2022)
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11
Impact of Sentence Representation Matching in Neural Machine Translation
In: Applied Sciences; Volume 12; Issue 3; Pages: 1313 (2022)
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12
A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter
In: Processes; Volume 10; Issue 3; Pages: 439 (2022)
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13
Application of Modern Digital Systems and Approaches to Business Process Management
In: Sustainability; Volume 14; Issue 3; Pages: 1697 (2022)
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14
BenSignNet: Bengali Sign Language Alphabet Recognition Using Concatenated Segmentation and Convolutional Neural Network
In: Applied Sciences; Volume 12; Issue 8; Pages: 3933 (2022)
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15
Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model
In: Sensors; Volume 22; Issue 2; Pages: 574 (2022)
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16
TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants
In: Applied Sciences; Volume 12; Issue 4; Pages: 1974 (2022)
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17
Factors Influencing Students’ Intention to Use E-Textbooks and Their Impact on Academic Achievement in Bilingual Environment: An Empirical Study Jordan
In: Information; Volume 13; Issue 5; Pages: 233 (2022)
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18
Active Binaural Auditory Perceptual System for a Socially Interactive Humanoid Robot
In: Engineering Proceedings; Volume 12; Issue 1; Pages: 83 (2022)
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19
Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible
In: NeuroSci; Volume 3; Issue 2; Pages: 226-244 (2022)
Abstract: This study focuses on the automatic decoding of inner speech using noninvasive methods, such as Electroencephalography (EEG). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain–computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of five vowels and six words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying five vowels and six words, respectively, in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection.
Keyword: brain–computer interface (BCI); Convolutional Neural Network (CNN); deep learning; electroencephalography (EEG); independent component analysis; inner speech; supervised learning
URL: https://doi.org/10.3390/neurosci3020017
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20
Multitask Pointer Network for Multi-Representational Parsing
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