Feature-based natural language processing for GSL synthesis

The work reported in this study is based on research that has been carried out while developing a sign synthesis system for Greek Sign Language (GSL) and involves theoretical linguistic analysis as well as lexicon and grammar resources derived from this analysis. We focus on the organisation of linguistic knowledge that initiates the multi-functional processing required to achieve sign generation performed by a virtual signer. In this context, structure rules and lexical coding support sign synthesis of GSL utterances, by exploitation of avatar technologies for the representation of the linguistic message. Sign generation involves two subsystems: a Greek-to-GSL conversion subsystem and a sign performance subsystem. The conversion subsystem matches input strings of written Greek-to-GSL structure patterns, exploiting Natural Language Processing (NLP) mechanisms. The sign performance subsystem uses parsed output of GSL structure patterns, enriched with sign-specific information, to activate a virtual signer for the performance of properly coded linguistic messages. Both the conversion and the synthesis procedure are based on adequately constructed electronic linguistic resources. Applicability of sign synthesis is demonstrated with the example of a Web-based prototype environment for GSL grammar teaching. © John Benjamins Publishing Company

SL-ReDu: Greek sign language recognition for educational applications. Project description and early results

We present SL-ReDu, a recently commenced innovative project that aims to exploit deep-learning progress to advance the state-of-the-art in video-based automatic recognition of Greek Sign Language (GSL), while focusing on the use-case of GSL education as a second language. We first briefly overview the project goals, focal areas, and timeline. We then present our initial deep learning-based approach for GSL recognition that employs efficient visual tracking of the signer hands, convolutional neural networks for feature extraction, and attention-based encoder-decoder sequence modeling for sign prediction. Finally, we report experimental results for small-vocabulary, isolated GSL recognition on the single-signer "Polytropon" corpus. To our knowledge, this work constitutes the first application of deep-learning techniques to GSL. © 2020 ACM

Greek Sign Language Recognition for the SL-ReDu Learning Platform

There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners' SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating in a signer-independent fashion and under realistic recording conditions. Here, we present an early version of a Greek Sign Language (GSL) recognizer that satisfies the above requirements, and integrate it within the SL-ReDu learning platform that constitutes a first in GSL with recognition functionality. We develop the recognition module incorporating state-of-the-art deep-learning based visual detection, feature extraction, and classification, designing it to accommodate a medium-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on a specifically recorded GSL corpus of multiple signers by a web-cam in non-studio conditions, and conduct both multi-signer and signer-independent recognition experiments, reporting high accuracies. Finally, we let student users evaluate the learning platform during GSL production exercises, reporting very satisfactory objective and subjective assessments based on recognition performance and collected questionnaires, respectively. © European Language Resources Association (ELRA), licensed under CC-BY-NC 4.0

Transitivity in RSL: A Corpus-Based Account

A recent typological study of transitivity by Haspelmath (2015) demonstrated that verbs can be ranked according to transitivity prominence, that is, according to how likely they are to be transitive cross-linguistically. This ranking can be argued to be cognitively rooted (based on the properties of the events and their participants) or frequency-related (based on the frequency of different types of events in the real world). Both types of explanation imply that the transitivity ranking should apply across modalities. To test it, we analysed transitivity of frequent verbs in the corpus of Russian Sign Language by calculating the proportion of overt direct and indirect objects and clausal complements. We found that transitivity as expressed by the proportion of overt direct objects is highly positively correlated with the transitive prominence determined cross-linguistically. We thus confirmed the modality-independent nature of transitivity ranking

Beyond the manual channel: Proceedings of the 6th Workshop on the Representation and Processing of Sign Languages: Beyond the Manual Channel

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The sl-redu environment for self-monitoring and objective learner assessment in greek sign language

Here we discuss the design and implementation features of a platform aiming to provide two distinct modules for self-monitoring and objective assessment of learners of the Greek Sign Language (GSL) as L2. The platform is designed according to user needs of both learners and instructors. It incorporates the educational content of the A0 and A1 levels of CEFR. The platform provides a user-friendly environment that guarantees improvement of learner’s skills, objectivity in learner assessment and enhanced SL knowledge grading. Active learner language production is assessed via an innovative SL recognition engine, while standard multimedia-based drills assess learners’ comprehension skills. © Springer Nature Switzerland AG 2021