Sharing sign language data online

2007 ◽  
Vol 12 (4) ◽  
pp. 535-562 ◽  
Author(s):  
Onno A. Crasborn ◽  
Johanna Mesch ◽  
Dafydd Waters ◽  
Annika Nonhebel ◽  
Els van der Kooij ◽  
...  
Keyword(s):  
Sign Language ◽  
Software Tools ◽  
Video Data ◽  
Ethical Aspects ◽  
Language Data ◽  
Language Corpus

This article describes how new technological possibilities allow sign language researchers to share and publish video data and transcriptions online. Both linguistic and technological aspects of creating and publishing a sign language corpus are discussed, and standards are proposed for both metadata and transcription categories specific to sign language data. In addition, ethical aspects of publishing video data of signers online are considered, and suggestions are offered for future corpus projects and software tools.

scholarly journals Data Augmentation for Human Keypoint Estimation Deep Learning based Sign Language Translation

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1257
Author(s):  
Chan-Il Park ◽  
Chae-Bong Sohn
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Data Augmentation ◽  
Language Translation ◽  
Video Data ◽  
Training Data ◽  
Learning Performance ◽  
Learning Technology ◽  
Learning Techniques ◽  
Language Data

Deep learning technology has developed constantly and is applied in many fields. In order to correctly apply deep learning techniques, sufficient learning must be preceded. Various conditions are necessary for sufficient learning. One of the most important conditions is training data. Collecting sufficient training data is fundamental, because if the training data are insufficient, deep learning will not be done properly. Many types of training data are collected, but not all of them. So, we may have to collect them directly. Collecting takes a lot of time and hard work. To reduce this effort, the data augmentation method is used to increase the training data. Data augmentation has some common methods, but often requires different methods for specific data. For example, in order to recognize sign language, video data processed with openpose are used. In this paper, we propose a new data augmentation method for sign language data used for learning translation, and we expect to improve the learning performance, according to the proposed method.

scholarly journals CORALSE: Design of a Corpus of Spanish Sign Language

10.29007/r1rt ◽  
2018 ◽  
Author(s):  
Ana-María Fernández Soneira ◽  
Inmaculada C. Báez Montero ◽  
Eva Freijeiro Ocampo
Keyword(s):  
Sign Language ◽  
Language Use ◽  
Subsequent Development ◽  
Research Activity ◽  
Sign Languages ◽  
Scientific Background ◽  
Different Types ◽  
Language Data ◽  
The Law ◽  
Language Corpus

The approval of the law for the recognition of Sign Languages and its subsequent development (together with the laws enacted by the regional governments and the work of universities and institutions such as CNLSE) has changed the landscape of the research activity carried out in the field of SL in Spain.In spite of these social advances, a corpus of Spanish Sign Language (LSE) has not yet been compiled. The average Sign Language corpus is traditionally composed of collections of annotated or tagged videos that contain written material aligned with the main Sign Language data.The compiling project presented here, CORALSE, proposes: 1) to collect a representative number of samples of language use; 2) to tag and transcribe the collected samples and build an online corpus; 3) to advance in the description of the grammar of LSE; 4) to provide the scientific background needed for the development of materials for educational purposes; and 5) to advance in the development of different types of LSE.

The FATE Landscape of Sign Language AI Datasets

2021 ◽  
Vol 14 (2) ◽  
pp. 1-45
Author(s):  
Danielle Bragg ◽  
Naomi Caselli ◽  
Julie A. Hochgesang ◽  
Matt Huenerfauth ◽  
Leah Katz-Hernandez ◽  
...  
Keyword(s):  
Sign Language ◽  
New Technology ◽  
Individual Data ◽  
Community Members ◽  
Sign Languages ◽  
Language Data ◽  
History Of ◽  
Language Technologies ◽  
And Storage ◽  
Access Mechanisms

Sign language datasets are essential to developing many sign language technologies. In particular, datasets are required for training artificial intelligence (AI) and machine learning (ML) systems. Though the idea of using AI/ML for sign languages is not new, technology has now advanced to a point where developing such sign language technologies is becoming increasingly tractable. This critical juncture provides an opportunity to be thoughtful about an array of Fairness, Accountability, Transparency, and Ethics (FATE) considerations. Sign language datasets typically contain recordings of people signing, which is highly personal. The rights and responsibilities of the parties involved in data collection and storage are also complex and involve individual data contributors, data collectors or owners, and data users who may interact through a variety of exchange and access mechanisms. Deaf community members (and signers, more generally) are also central stakeholders in any end applications of sign language data. The centrality of sign language to deaf culture identity, coupled with a history of oppression, makes usage by technologists particularly sensitive. This piece presents many of these issues that characterize working with sign language AI datasets, based on the authors’ experiences living, working, and studying in this space.

Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
HyeonJung Park ◽  
Youngki Lee ◽  
JeongGil Ko
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Data Augmentation ◽  
Language Translation ◽  
Mobile Platforms ◽  
Depth Cameras ◽  
Language Data ◽  
In The Wild ◽  
Environmental Robustness ◽  
Cloud Servers

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.

Current and future methodologies for quantitative analysis of information transfer in sign language and gesture data

2017 ◽  
Vol 40 ◽  
Author(s):  
Evie Malaia
Keyword(s):  
Quantitative Analysis ◽  
Data Collection ◽  
Sign Language ◽  
Motion Capture ◽  
Information Transfer ◽  
State Of The Art ◽  
Video Data ◽  
Noninvasive Methods ◽  
Video Recordings ◽  
Biological Differentiation

AbstractState-of-the-art methods of analysis of video data now include motion capture and optical flow from video recordings. These techniques allow for biological differentiation between visual communication and noncommunicative motion, enabling further inquiry into neural bases of communication. The requirements for additional noninvasive methods of data collection and automatic analysis of natural gesture and sign language are discussed.

Gloss annotations in the Swedish Sign Language Corpus

2015 ◽  
Vol 20 (1) ◽  
pp. 102-120 ◽  
Author(s):  
Johanna Mesch ◽  
Lars Wallin
Keyword(s):  
Sign Language ◽  
Core Data ◽  
Annotation System ◽  
Language Structure ◽  
Paper Cover ◽  
Data Source ◽  
Language Corpus

The Swedish Sign Language Corpus (SSLC) was compiled during the years 2009–2011 and consists of video-recorded conversations with 42 informants between the ages of 20 and 82 from three separate regions in Sweden. The overall aim of the project was to create a corpus of Swedish Sign Language (SSL) that could provide a core data source for research on language structure and use, as well as for dictionary work. A portion of the corpus has been annotated with glosses for signs and Swedish translations, and annotation of the entire corpus is ongoing. In this paper, we outline our scheme for gloss annotation and discuss issues that are relevant in creating the annotation system, with unique glosses for lexical signs, fingerspelling and productive signs. The annotation guidelines discussed in this paper cover both one- and two-handed signs in SSL, based on 33,600 tokens collected for the SSLC.

scholarly journals METHOD OF MULTI-MODAL VIDEO ANALYSIS OF HAND MOVEMENTS FOR AUTOMATIC RECOGNITION OF ISOLATED SIGNS OF RUSSIAN SIGN LANGUAGE

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 7-13
Author(s):  
А. Axyonov ◽  
D. Ryumin ◽  
I. Kagirov
Keyword(s):  
Sign Language ◽  
Video Sequence ◽  
Recognition Accuracy ◽  
Video Data ◽  
New Method ◽  
Visual Features ◽  
Temporal Data ◽  
Sign Recognition ◽  
Spatio Temporal ◽  
Extract Information

Abstract. This paper presents a new method for collecting multimodal sign language (SL) databases, which is distinguished by the use of multimodal video data. The paper also proposes a new method of multimodal sign recognition, which is distinguished by the analysis of spatio-temporal visual features of SL units (i.e. lexemes). Generally, gesture recognition is a processing of a video sequence, which helps to extract information on movements of any articulator (a part of the human body) in time and space. With this approach, the recognition accuracy of isolated signs was 88.92%. The proposed method, due to the extraction and analysis of spatio-temporal data, makes it possible to identify more informative features of signs, which leads to an increase in the accuracy of SL recognition.

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