Sign language recognition with transformer networks

Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7% on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation

Natural Language Processing: A Look Into How Computers Understand Human Language

The semantic interpretation of the human language is very complex and diverse making natural language processing an interesting task for researchers and engineers. Natural language processing is a subfield of machine learning focusing on enabling computers to understand and process human languages. Although computers do not have the same intuitive understanding of natural language like humans do, recent advances in machine learning have enabled computers to perform many useful things with natural language like text classification, language modeling, speech recognition, and question answering. Computers are able to accomplish these tasks by learning the deep contextual representations of words including both the syntax and semantics. Through the use of recurrent neural networks, long short-term memory units, temporal convolution networks, and different language embedding models, computers have made significant strides in their ability to interpret and understand human language. With large volumes of textual data available and the need to structure the unstructured data source that is human language, the area of natural language processing will continue to be of interest.https://ecommons.udayton.edu/stander_posters/2706/thumbnail.jp

Identity and Autonomy in a Human Complex System

The work presented here is centred on the notions of language, of code as well as the interactions that allow to take into account the complex relations between different types of entities, actors, ... corresponding to the embedded cognitive networks . At this level, questions about the identity and the heterogeneity of actors particularly important to the globalisation phenomena can be examined through the negotiation mechanisms and collective decisions. The multiplicity of cognitive shortcuts used, related to the autonomy of actors and institutions or to their interactions, makes it possible to take into account the complexity of human systems.autonomy; cognitive shortcut; complex mediation; embeddeness; identity