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Semantic Feature Extraction Using SBERT for Dementia Detection
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In: Brain Sciences; Volume 12; Issue 2; Pages: 270 (2022)
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Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
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In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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Machine Learning of Motion Statistics Reveals the Kinematic Signature of the Identity of a Person in Sign Language
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In: ISSN: 2296-4185 ; Frontiers in Bioengineering and Biotechnology ; https://hal.archives-ouvertes.fr/hal-03298752 ; Frontiers in Bioengineering and Biotechnology, Frontiers, 2021, 9, ⟨10.3389/fbioe.2021.710132⟩ (2021)
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| 4 |
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
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In: IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03003469 ; IEEE Spoken Language Technology Workshop, Jan 2021, Virtual, China (2021)
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| 5 |
Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy
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In: Entropy; Volume 24; Issue 1; Pages: 22 (2021)
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Discriminative feature modeling for statistical speech recognition ...
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| 7 |
A Review of Urdu Sentiment Analysis with Multilingual Perspective: A Case of Urdu and Roman Urdu Language
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In: Computers; Volume 11; Issue 1; Pages: 3 (2021)
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| 8 |
A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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| 9 |
Comparing vector document representation methods for authorship identification ; Comparando métodos de representação vectorial de documentos para identificação de autoria
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Quintanilla, Pamela Rosy Revuelta. - : Biblioteca Digital de Teses e Dissertações da USP, 2021. : Universidade de São Paulo, 2021. : Instituto de Matemática e Estatística, 2021
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| 10 |
Extracting Human Behaviour and Personality Traits from Social Media
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| 11 |
A Thesaurus Based Semantic Relation Extraction for Agricultural Corpora
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In: IFIP Advances in Information and Communication Technology ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS) ; https://hal.inria.fr/hal-03434803 ; 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.99-111, ⟨10.1007/978-3-030-63467-4_8⟩ (2020)
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| 12 |
Towards a Term Clustering Framework for Modular Ontology Learning
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In: Knowledge Discovery, Knowledge Engineering and Knowledge Management ; https://hal.archives-ouvertes.fr/hal-03063773 ; Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp.178-201, 2020, ⟨10.1007/978-3-030-49559-6_9⟩ (2020)
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| 18 |
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
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In: https://ieeexplore.ieee.org/ (2020)
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Abstract:
Vision-based sign language recognition aims at helping the deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words. Due to the limited vocabulary size, models learned from those datasets cannot be applied in practice. In this paper, we introduce a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers. This dataset will be made publicly available to the research community. To our knowledge,it is by far the largest public ASL dataset to facilitate word-level sign recognition research. Based on this new large-scale dataset, we are able to experiment with several deep learning methods for wordlevel sign recognition and evaluate their performances in large scale scenarios. Specifically we implement and compare two different models,i.e., (i) holistic visual appearance based approach, and (ii) 2D human pose based approach. Both models are valuable baselines that will benefit the community for method benchmarking. Moreover, we also propose a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose-based method. Our results show that pose-based and appearance-based models achieve comparable performances up to 62.63% at top-10 accuracy on 2,000 words/glosses, demonstrating the validity and challenges of our dataset. Our dataset and baseline deep models are available at https://dxli94.github.io/WLASL/. ; Dongxu Li , Cristian Rodriguez Opazo, Xin Yu, Hongdong Li
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Keyword:
Assistive technology; Feature extraction; Gesture recognition
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URL: https://hdl.handle.net/2440/133224
https://doi.org/10.1109/wacv45572.2020.9093512
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| 19 |
Receptive field transformations of the optimal HSNN predicts transformations observed along the ascending auditory pathway.
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| 20 |
Spoofing Countermeasures for Voice Biometric System: Feature Extraction, Modelling and Compensation
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