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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 21 – 26 of 26 21 BERT-Based Arabic Social Media Author Profiling ... Zhang, Chiyu; Abdul-Mageed, Muhammad. - : arXiv, 2019 BASE Show details 22 Multi-Task Bidirectional Transformer Representations for Irony Detection ... Zhang, Chiyu; Abdul-Mageed, Muhammad. - : arXiv, 2019 BASE Show details 23 DiaNet: BERT and Hierarchical Attention Multi-Task Learning of Fine-Grained Dialect ... Abdul-Mageed, Muhammad; Zhang, Chiyu; Elmadany, AbdelRahim. - : arXiv, 2019 BASE Show details 24 Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts ... Saha, Pramit; Abdul-Mageed, Muhammad; Fels, Sidney. - : arXiv, 2019 BASE Show details 25 SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning ... Saha, Pramit; Abdul-Mageed, Muhammad; Fels, Sidney. - : arXiv, 2019 Abstract: Speech-related Brain Computer Interface (BCI) technologies provide effective vocal communication strategies for controlling devices through speech commands interpreted from brain signals. 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. Our novel approach exploits predicted articulatory information of six phonological categories (e.g., nasal, bilabial) as an intermediate step for classifying the phonemes and words, thereby finding discriminative signal responsible for natural speech synthesis. The proposed network is composed of hierarchical combination of spatial and temporal CNN cascaded with a deep autoencoder. Our best models on the KARA database achieve an average accuracy of 83.42% across the six different binary phonological classification tasks, and 53.36% for the individual token identification task, significantly outperforming our baselines. ... : Under review in INTERSPEECH 2019. arXiv admin note: text overlap with arXiv:1904.04358 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Machine Learning stat.ML; Sound cs.SD URL: https://arxiv.org/abs/1904.05746 https://dx.doi.org/10.48550/arxiv.1904.05746 BASE Hide details 26 SAMAR: Subjectivity and sentiment analysis for Arabic social media Abdul-Mageed, Muhammad; Diab, Mona; Kübler, Sandra In: Computer speech and language. - Amsterdam [u.a.] : Elsevier 28 (2014) 1, 20-37 OLC Linguistik Show details Page: 1 2

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