Sign language technology; Computer-/mobile assisted language testing; Sign language recognition; Common European Framework of Reference; Sign assessment
Herman Rosalind, Grove Nicola, Haug Tobias, Mann Wolfgang, Prinz Philip (2020), Chapter 4. The assessment of signed languages, in Morgan Gary (ed.), John Benjamins Publishing Company, Amsterdam, 53-72.
TornaySandrine, CamgozNecati Cihan, BowdenRichard, Magimai-DossMathew (2020), A Phonology-based Approach for Isolated Sign Production Assessment in Sign Language, in ICMI '20: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION
, NetherlandsAssociation for Computing Machinery, New York, USA.
TornaySandrine, AranOya, Magimai-DossMathew (2020), An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition, in 12th Conference on Language Resources and Evaluation (LREC 2020)
, MarseilleEuropean Language Resources Association (ELRA), France.
HaugTobias, MannW (2020), Is Online Testing the Future of Signed Language Assessment?, in New Directions Online Conference 2020
, British Council, UK.
Tornay Sandrine, Razavi Marzieh, Magimai-Doss Mathew (2020), Towards Multilingual Sign Language Recognition, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
, Idiap, Martigny.
Haug Tobias, Ebling Sarah (2019), Using Open-Source Software for Sign Language Learning and Assessment, in International Journal of Emerging Technologies in Learning (iJET)
, 14(19), 188-196.
TornaySandrine, Magimai-DossMathew (2019), Subunits Inference and Lexicon Development Based on Pairwise Comparison of Utterances and Signs, in Information
, 10(10), 298.
Koller Oscar, Camgoz Necati Cihan, Hermann Ney, Bowden Richard (2019), Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos, in Transactions on Pattern Analysis and Machine Intelligence
, 42(9), 2306.
Tornay Sandrine, Razavi Marzieh, Magimai-Doss Mathew (2019), Data-Driven Movement Subunit Extraction from Skeleton Information for Modeling Signs and Gestures
, Idiap Research Institute, Idiap Internal Research Report Idiapl-RR-02-2019, Martigny.
Tornay Sandrine, Razavi M, Camgoz Necati Cihan, Bowden Richard, Magimai-Doss Mathew (2019), HMM Based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing, in In Proc. ICASSP 2019
, In Proc. ICASSP 2019, UK.
Haug Tobias, Ebling Sarah, Boyes Braem Penny, Sidler-Miserez Sandra, Tissi Katja (2019), Sign Language Learning and Assessment in German Switzerland: Exploring the potential of vocabulary size tests for Swiss German Sign Language., in Language Education & Assessment
, 2(1), 20.
StollStephanie, CamgozNecati Cihan, HadfieldSimon, BowdenRichard (2019), Text2Sign: Towards Sign Language Production using Neural Machine Translation and Generative Adversarial Networks, in International Journal of Computer Vision (IJCV)
, 128, 891.
Koller Oscar, Zargaran Sepehr, Ney Hermann, Bowden Richard (2018), Deep Sign: Enabling Robust Statistical Continuous Sign Language Recognition via Hybrid CNN-HMMs, in International Journal of Computer Vision
, 126(12), 1311-1325.
Haug Tobias, Ebling Sarah, Boyes Braem Penny, Tissi Katja, Sidler-Miserez Sandra (2018), Automatic sign language recognition for sign language assessment, in Conference on Technology-Based Language Assessment 2018
, European Association for Language Testing and Assessment (EALTA), Bochum, Germany.
Arter Lisa (2018), Eine Untersuchung der Verwendungsfrequenz von Gebärdenvarianten bei L1- und L2-Benutzern im Rahmen des SMILE-Projektes
, HfH, BA Thesis, Zurich.
HaugTobias (2018), Gebärdensprachtests in der Deutschschweiz, in Hörgeschädigtenpädagogik
, 72(4), 199-204.
Hadfield Simon James, Lebeda Karel, Bowden Richard (2018), HARD-PnP: PnP Optimization Using a Hybrid Approximate Representation, in IEEE Transactions on Pattern Analysis and Machine Intelligence
, 41(3), 768-774.
Camgoz Necati Cihan, Hadfield Simon, Koller Oscar, Ney Hermann, Bowden Richard (2018), Neural Sign Language Translation, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR18)
, IEEE, Salt Lake City.
Rittiner Laura (2018), Phonologische Produktionsfehler bei L2-Lernern der Deutschschweizer Gebärdensprache: Eine Analyse und Kategorisierung von phonologischen Produktionsfehlern im Rahmen des SMILE-Projektes
, HfH - BA Thesis, Zurich.
Stoll Stephanie, Camgoz Necati Cihan, Hadfield Simon, Bowden Richard (2018), Sign Language Production using Neural Machine Translation and Generative Adversarial Networks, in British Machine Vision Conference
, British Machine Vision Conference, Newcastle, UK.
Ebling Sarah, Camgöz Necati Cihan, Boyes Braem Penny, Tissi Katja, Sidler-Miserez Sandra, Stoll Stephanie, Hadfield Simon, Haug Tobias, Bowden Richard, Tornay Sandrine, Razavi Marzieh, Magimai-Doss Mathew (2018), SMILE Swiss German Sign Language Dataset, in Proceedings of 11th International Conference on Language Resources and Evaluation (LREC 2018)
, ELRA, Japan.
Schlumpf Cheryl (2018), Unterschiede von Produktionsfehlern in Bezug auf die manuellen Komponenten bei L2-Benutzern des SMILE-Projekts
, HfH, BA Thesis, Zurich.
Camgoz Necati Cihan, Hadfield Simon, Bowden Richard (2017), Particle Filter Based Probabilistic Forced Alignment for Continuous Gesture Recognition, in Proc. Chalearn 2017, IEEE International Conference on Computer Vision Workshops (ICCVW)
, IEEE, Venice, Italy.
Camgoz Necati Cihan, Hadfield Simon, Koller Oscar, Bowden Richard (2017), SubUNets: End-to-end Hand Shape and Continuous Sign Language Recognition, in Proceedings of IEEE Int. Conf. Computer Vision (ICCV)
, IEEE, Venice.
Lebeda Karel, Hadfield Simon, Bowden Richard (2017), TMAGIC: A Model-free 3D Tracker, in IEEE Transactions on Image Processing
, 26(9), 4378-4388.
Hadfield S, Lebeda K, Bowden R (2017), Stereo Reconstruction Using Top-down Cues. Computer Vision and Image Understanding, in Computer Vision and Image Understanding
, 157, 206-222.
Haug Tobias (2017), Development and Evaluation of Two Vocabulary Tests for Swiss German Sign Language
, University of Lancaster, Masters Thesis, UK.
Hadfield S, Lebeda K, Bowden R (2017), Hollywood 3D: What are the best 3D features for Action Recognition?, in International Journal of Computer Vision
, 121(1), 95-110.
Koller O, Zargaran S, Ney H, Bowden R (2016), Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition, in Proceedings of British Machine Vision Conference
, York, UKBritish Machine Vision Association (BMVA), BMVA Press.
Lebeda K, Hadfield S, Bowden R (2016), Direct-from-Video: Unsupervised NRSfM, in Proceedings of the ECCV workshop on Recovering 6D Object Pose Estimation
, ECCV, ECCV.
Ebling S, Boyes Braem P, Tissi K, Sidler-Miserez S, Haug T (2016), Selecting items for the DSGS vocabulary production test
, Interkantonale Hochschule für Heilpädagogik, HfH, Zurich.
Camgoz N C, Hadfield S, Koller O, Bowden R (2016), Using Convolutional 3D Neural Networks for User-Independent Continuous Gesture Recognition, in Proceedings of IEEE International Conference of Pattern Recognition (ICPR), ChaLearn Workshop, 2016
, Cancun, MexicoIAPR, IEEE, IEEEXplore.
SaundersBen, CamgozNecati Cihan, BowdenRichard, Adversarial Training for Multi-Channel Sign Language Production, in The 31st British Machine Vision Virtual Conference (BMVC)
, University of Surrey, Guildford.
CamgozNecati Cihan, KollerOscar, HadfieldSimon, BowdenRichard, Multi-channel transformers for multi-articulatory sign language translation, in European Conference on Computer Vision Workshops (ECCVW), ACVR Workshop
, Cornell University, UK.
CamgozNecati Cihan, Neural Sign Language Recognition and Translation
, University of Surrey, Surrey.
SaundersBen, CamgozNecati Cihan, BowdenRichard, Progressive Transformers for End-to-End Sign Language Production, in Computer Vision and Pattern Recognition
, Cornell University, UK.
Camgoz Necati Cihan, Koller Oscar, Hadfield Simon, Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
, IEEE, Germany.
Ebling Sarah, Tissi Katja, Sidler-Miserez Sandra, Schlumpf Cheryl, Boyes Braem Penny, Single-parameter and parameter combination errors in L2 productions of Swiss German Sign Language, in Sign Language & Linguistics
CamgozNecati Cihan, VarolGül, AlbanieSamuel, FoxNeil, BowdenRichard, ZissermanAndrew, CormierKearsy, SLRTP 2020: The Sign Language Recognition, Translation & Production Workshop, in European Conference on Computer Vision Workshops (ECCVW)
, Springer, UK.
HaugTobias, Boers-ViskerE., Van den BogaerdeB., Testing sign language learners, Routledge, UK, 200.
Ebling Sarah, Hegelheimer Volker, Camgoz Necati Cihan, Bowden Richard, Use of New Technologies in L2 Assessment, Oxford University Press, UK.
Ebling Sarah, Camgoz Necati Cihan, Bowden Richard, Use of new technologies in L2 sign language assessment, Oxford: OUP, Oxford.
Tissi Katja, Sidler-Miserez Sandra, Ebling Sarah, Boyes Braem Penny, What’s wrong? Rethinking the concept of 'citation forms', in International Conference on Sign Language Acquisition (ICSLA)
, International Conference on Sign Language Acquisition (ICSLA), Istanbul, Turkey.
The goal of the proposed project SMILE is to pioneer an assessment system for Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) using automatic sign language recognition technology. To achieve this goal, this project uses a multidisciplinary framework that follows two strands of research, one on sign language technology and one on sign assessment with a common link to sign language linguistics. A single institution alone cannot do the very different kinds of research involved in this project. Therefore, a project consortium of three partner institutes with complementary expertise has been built:1. The Idiap Research Institute (Martigny, Switzerland) will coordinate the project and will contribute to the project by developing a novel automatic sign language assessment and feedback approach taking inspirations from a speech recognition approach that was developed under SNSF project FlexASR.2.The Hochschule für Heilpädagogik (HfH in Zurich) will bring its expertise in sign language assessment and sign linguistics (through collaboration with Center for Sign Language Research, FZG, Basel). In addition, the HfH will play a central role in connecting the real world of L2 learners and the deaf community in the German part of Switzerland to the project.3. The University of Surrey (England) will bring to the project its longstanding expertise in sign language technology, visual data acquisition and computer vision and in particular its wide experience in state-of-the-art sign language technology research through European-level projects such as DictaSign.To achieve the end-goals, the project is organized as three sub-projects: 1. Resources and Tools, which will deal with creation of requisite DSGS sign language resources and tools. 2. Sign Language Technology, which will deal with development of an automatic sign assessment system with feedback based on sign language recognition/verification and sign production.3. Assessment of L2 Learners and Feedback, which will develop and standardize a vocabulary test that can be aligned with levels A1 and A2 of the Common European Framework of Reference for Languages (CEFR), and will evaluate automatic sign language assessment system w.r.t human assessment. The SMILE project will involve not only experienced and internationally known researchers in their respective fields, but also young hearing and Deaf team members. The results of the project are expected to have an echo in the larger Deaf community -- not only through the involvement of many Swiss German Deaf signers with the project as subjects, but also because the national Swiss Deaf Association has recently decided to align its sign language curricula to the levels of the Common European Framework of Reference for Languages (CEFR). This SMILE project follows the CEFR approach by developing an assessment system that tests the production of vocabulary of DSGS at level A1 with first time integration of new technologies for sign language. SMILE will thus lay an advanced platform for teaching and learning systems, both specifically for DSGS and as a model for other sign languages.