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The Impact of Removing Head Movements on Audio-visual Speech Enhancement
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In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.inria.fr/hal-03551610 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Signal Processing Society, May 2022, Singapore, Singapore. pp.1-5 (2022)
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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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BBC-Oxford British Sign Language Dataset
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In: https://hal.archives-ouvertes.fr/hal-03516444 ; 2022 (2022)
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Can machines learn to see without visual databases?
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In: https://hal.archives-ouvertes.fr/hal-03526569 ; 2022 (2022)
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Large-scale Bilingual Language-Image Contrastive Learning ...
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Bridging Video-text Retrieval with Multiple Choice Questions ...
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Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
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An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production ...
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Expression-preserving face frontalization improves visually assisted speech processing ...
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WLASL-LEX: a Dataset for Recognising Phonological Properties in American Sign Language ...
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Abstract:
Signed Language Processing (SLP) concerns the automated processing of signed languages, the main means of communication of Deaf and hearing impaired individuals. SLP features many different tasks, ranging from sign recognition to translation and production of signed speech, but has been overlooked by the NLP community thus far. In this paper, we bring to attention the task of modelling the phonology of sign languages. We leverage existing resources to construct a large-scale dataset of American Sign Language signs annotated with six different phonological properties. We then conduct an extensive empirical study to investigate whether data-driven end-to-end and feature-based approaches can be optimised to automatically recognise these properties. We find that, despite the inherent challenges of the task, graph-based neural networks that operate over skeleton features extracted from raw videos are able to succeed at the task to a varying degree. Most importantly, we show that this performance pertains even on ... : Accepted at ACL 2022 main conference ...
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Keyword:
Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2203.06096 https://arxiv.org/abs/2203.06096
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Modeling Intensification for Sign Language Generation: A Computational Approach ...
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Keypoint based Sign Language Translation without Glosses ...
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A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition ...
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A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation ...
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Including Facial Expressions in Contextual Embeddings for Sign Language Generation ...
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Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production ...
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Statistical and Spatio-temporal Hand Gesture Features for Sign Language Recognition using the Leap Motion Sensor ...
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Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition ...
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