@inproceedings{martinezguevara:24027:sign-lang:lrec,
  author    = {Mart{\'i}nez-Guevara, Niels and Curiel, Arturo},
  title     = {Quantitative Analysis of Hand Locations in both Sign Language and Non-linguistic Gesture Videos},
  pages     = {225--234},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24027.html},
  abstract  = {This paper explores whether measurable quantitative linguistic relationships are readily apparent in the use of space of three different Sign Languages (SLs): British Sign Language (BSL), Dutch Sign Language (NGT) and Mexican Sign Language (LSM). To this end, three SL datasets were collected; one for each of the languages of interest. Informative video frames were extracted from the collected datasets, which in turn were automatically processed to detect hand locations. The obtained information was analyzed through statistical methods, and compared against a dataset of non-linguistic gestural communication: the latter, in an effort to observe whether space-use differs between linguistic and non-linguistic gestures. The results show that meaningful gestures---regardless of whether they are deemed linguistic or not---seem to induce a spatial hierarchy around the gesturer, disproportionately favoring certain areas during articulation. SLs in particular seem to exert pressure on those areas to become more efficient, as signers appear to concentrate hand activity over more cohesive regions than non-signers. In addition, these results point towards an indirect relationship between culturally-recognized gestures and their surrounding SLs, showing that there is still work to be done on the exploration of iconicity and its effects on gestural communication.}
}

@inproceedings{picron:24021:sign-lang:lrec,
  author    = {Picron, Frankie and Van Landuyt, Davy and Omardeen, Rehana and Efthimiou, Eleni and Wolfe, Rosalee and Fotinea, Stavroula-Evita and Goulas, Theodoros and Tismer, Christian and Kopf, Maria and Hanke, Thomas},
  title     = {The {EASIER} Mobile Application and Avatar End-User Evaluation Methodology},
  pages     = {276--281},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24021.html},
  abstract  = {Here we report on the methodological approach adopted for the end-user evaluation studies carried out during the lifecycle of the EASIER project, focusing on the project's mobile app and avatar technologies. Evaluation was performed in two cycles and involved both deaf signers' and hearing sign language (SL) experts' groups from five SLs to provide user feedback, which served as a reference to base the next development steps of the respective EASIER components. With this goal in mind, priorities were (i) to exploit information gathered via focus group discussions after (ii) presenting evaluators with the technological components and related questionnaires fully accessible to signers to maximize feedback and underline the importance of user involvement in the development of the technology.}
}

@inproceedings{schulder:24034:sign-lang:lrec,
  author    = {Schulder, Marc and Bigeard, Sam and Kopf, Maria and Hanke, Thomas and Kuder, Anna and W{\'o}jcicka, Joanna and Mesch, Johanna and Bj{\"o}rkstrand, Thomas and Vacalopoulou, Anna and Vasilaki, Kyriaki and Goulas, Theodoros and Fotinea, Stavroula-Evita and Efthimiou, Eleni},
  title     = {Signs and Synonymity: Continuing Development of the {Multilingual} {Sign} {Language} {Wordnet}},
  pages     = {343--353},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources},
  maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)},
  publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}},
  address   = {Torino, Italy},
  day       = {25},
  month     = may,
  year      = {2024},
  isbn      = {978-2-493814-30-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24034.html},
  abstract  = {The Multilingual Sign Language Wordnet is the first publicly available wordnet resource for sign languages. It is a growing multilingual resource providing data for eight sign languages to date. During the initial phase of its creation, the focus lay on producing the infrastructure to support various languages and to produce initial sets of content for them. This article represents the start of the second phase, in which the focus is moved to establishing overlapping coverage across the different sign languages. Building on the data produced so far, a new feature to assist annotation is introduced which leverages established partial synonymy between signs (inter- and cross-lingually) to discover likely additional synonymies. Other improvements to the annotation interface and workflow build directly on the experiences from the first phase. Working with the updated annotation interface, new data is produced for Polish Sign Language, Greek Sign Language and Swedish Sign Language.}
}

@inproceedings{jui:22018:sign-lang:lrec,
  author    = {Jui, Tonni Das and Bejarano, Gissella and Rivas, Pablo},
  title     = {A Machine Learning-based Segmentation Approach for Measuring Similarity between Sign Languages},
  pages     = {94--101},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-86-3},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22018.html},
  abstract  = {Due to the lack of more variate, native and continuous datasets, sign languages are low-resources languages that can benefit from multilingualism in machine translation. In order to analyze the benefits of approaches like multilingualism, finding the similarity between sign languages can guide better matches and contributions between languages. However, calculating the similarity between sign languages again implies a laborious work to measure how close or distant signs are and their respective contexts. For that reason, we propose to support the similarity measurement between sign languages through a video-segmentation-based machine learning model that will quantify this match among signs of different countries' sign languages. Using a machine learning approach the similarity measurement process can run more smoothly, compared to a more manual approach. We use a pre-trained temporal segmentation model for British Sign Language (BSL). We test it on three datasets, an American Sign Language (ASL) dataset, an Indian Sign Language (ISL), and an Australian Sign Language (AUSLAN) dataset. We hypothesize that the percentage of segmented and recognized signs by this machine learning model can represent the percentage of overlap or similarity between British and the other three sign languages. In our ongoing work, we evaluate three metrics considering Swadesh's and Woodward's list and their synonyms. We found that our intermediate-strict metric coincides with a more classical analysis of the similarity between British and American Sign Language, as well as with the classical low measurement between Indian and British sign languages. On the other hand, our similarity measurement between British and Australian Sign language just holds for part of the Australian Sign Language and not the whole data sample.}
}

@inproceedings{power:22021:sign-lang:lrec,
  author    = {Power, Justin M. and Quinto-Pozos, David and Law, Danny},
  title     = {Signed Language Transcription and the Creation of a Cross-linguistic Comparative Database},
  pages     = {173--180},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-86-3},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22021.html},
  abstract  = {As the availability of signed language data has rapidly increased, sign scholars have been confronted with the challenge of creating a common framework for the cross-linguistic comparison of the phonological forms of signs. While transcription techniques have played a fundamental role in the creation of cross-linguistic comparative databases for spoken languages, transcription has featured much less prominently in sign research and lexicography. Here we report the experiences of the Sign Change project in using the signed language transcription system HamNoSys to create a comparative database of basic vocabulary for thirteen signed languages. We report the results of a small-scale study, in which we measured (i) the average time required for two trained transcribers to complete a transcription and (ii) the similarity of their independently produced transcriptions. We find that, across the two transcribers, the transcription of one sign required, on average, one minute and a half. We also find that the similarity of transcriptions differed across phonological parameters. We consider the implications of our findings about transcription time and transcription similarity for other projects that plan to incorporate transcription techniques.}
}

@inproceedings{woll:22007:sign-lang:lrec,
  author    = {Woll, Bencie and Fox, Neil and Cormier, Kearsy},
  title     = {Segmentation of Signs for Research Purposes: Comparing Humans and Machines},
  pages     = {198--201},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-86-3},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22007.html},
  abstract  = {Sign languages such as British Sign Language (BSL) are visual languages which lack standard writing systems. Annotation of sign language data, especially for the purposes of machine readability, is therefore extremely slow. Tools to help automate and thus speed up the annotation process are very much needed. Here we test the development of one such tool (VIA-SLA), which uses temporal convolutional networks (Renz et al., 2021a, b) for the purpose of segmenting continuous signing in any sign language, and is designed to integrate smoothly with ELAN, the widely used annotation software for analysis of videos of sign language. We compare automatic segmentation by machine with segmentation done by a human, both in terms of time needed and accuracy of segmentation, using samples taken from the BSL Corpus (Schembri et al., 2014). A small sample of four short video files is tested (mean duration 25 seconds). We find that mean accuracy in terms of number and location of segmentations is relatively high, at around 78{\%}. This preliminary test suggests that VIA-SLA promises to be very useful for sign linguists.}
}

@inproceedings{sisto-etal-2022-challenges:lrec,
  author    = {De Sisto, Mirella and Vandeghinste, Vincent and Egea G{\'o}mez, Santiago and De Coster, Mathieu and Shterionov, Dimitar},
  title     = {Challenges with Sign Language Datasets for Sign Language Recognition and Translation},
  pages     = {2478--2487},
  editor    = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios},
  booktitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {20--25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-72-6},
  language  = {english},
  url       = {https://aclanthology.org/2022.lrec-1.264},
  abstract  = {Sign Languages (SLs) are the primary means of communication for at least half a million people in Europe alone. However, the development of SL recognition and translation tools is slowed down by a series of obstacles concerning resource scarcity and standardization issues in the available data. The former challenge relates to the volume of data available for machine learning as well as the time required to collect and process new data. The latter obstacle is linked to the variety of the data, i.e., annotation formats are not unified and vary amongst different resources. The available data formats are often not suitable for machine learning, obstructing the provision of automatic tools based on neural models. In the present paper, we give an overview of these challenges by comparing various SL corpora and SL machine learning datasets. Furthermore, we propose a framework to address the lack of standardization at format level, unify the available resources and facilitate SL research for different languages. Our framework takes ELAN files as inputs and returns textual and visual data ready to train SL recognition and translation models. We present a proof of concept, training neural translation models on the data produced by the proposed framework.}
}

@inproceedings{isard:20037:sign-lang:lrec,
  author    = {Isard, Amy},
  title     = {Approaches to the Anonymisation of Sign Language Corpora},
  pages     = {95--100},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2020} 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives},
  maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {16},
  month     = may,
  year      = {2020},
  isbn      = {979-10-95546-54-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20037.html},
  abstract  = {In this paper we survey the state of the art for the anonymisation of sign language corpora. We begin by exploring the motivations behind anonymisation and the close connection with the issue of ethics and informed consent for corpus participants. We detail how the the names which should be anonymised can be identified. We then describe the processes which can be used to anonymise both the video and the annotations belonging to a corpus, and the variety of ways in which these can be carried out. We provide examples for all of these processes from three sign language corpora in which anonymisation of the data has been performed.}
}

@inproceedings{liang:20031:sign-lang:lrec,
  author    = {Liang, Xing and Woll, Bencie and Epaminondas, Kapetanios and Angelopoulou, Anastasia and Al-Batat, Reda},
  title     = {Machine Learning for Enhancing Dementia Screening in Ageing Deaf Signers of {British} {Sign} {Language}},
  pages     = {135--138},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2020} 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives},
  maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {16},
  month     = may,
  year      = {2020},
  isbn      = {979-10-95546-54-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20031.html},
  abstract  = {Ageing trend in populations is correlated with increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. With this in mind, researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In this paper, we introduce  an automatic dementia screening system for ageing Deaf signers of British Sign Language (BSL), using Convolutional Neural Networks (CNN), by analysing the sign space envelope and facial expression of BSL signers using normal 2D videos from BSL corpus. Our approach firstly establishes an accurate real-time hand trajectory tracking model together with a real-time landmark facial motion analysis model to identify differences in sign space envelope and facial movement as the keys to identifying language changes associated with dementia. Based on the differences in patterns obtained from facial and trajectory motion data, CNN models (ResNet50/VGG16) are fine-tuned using Keras deep learning models to incrementally identify and improve dementia recognition rates. We report the results for two methods using different modalities (sign trajectory and facial motion), together with the performance comparisons between different deep learning CNN models in ResNet50 and VGG16. The experiments show the effectiveness of our deep learning based approach in terms of sign space tracking, facial motion tracking and early stage dementia performance assessment tasks. The results are validated against cognitive assessment scores as of our ground truth data with a test set performance of 87.88{\%}. The proposed system has potential for economical, simple, flexible, and adaptable assessment of other acquired neurological impairments associated with motor changes, such as stroke and Parkinson's disease in both hearing and Deaf people.}
}

@inproceedings{cassidy-etal-2018-signbank:lrec,
  author    = {Cassidy, Steve and Crasborn, Onno and Nieminen, Henri and Stoop, Wessel and Hulsbosch, Micha and Even, Susan and Komen, Erwin and Johnston, Trevor},
  title     = {{S}ignbank: Software to Support Web Based Dictionaries of Sign Language},
  pages     = {2359--2364},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo,  H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu},
  booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Miyazaki, Japan},
  day       = {7--12},
  month     = may,
  year      = {2018},
  isbn      = {979-10-95546-00-9},
  language  = {english},
  url       = {https://aclanthology.org/L18-1374},
  abstract  = {Signbank is a web application that was originally built to support the Auslan Signbank on-line web dictionary, it was an Open Source re-implementation of an earlier version of that site. The application provides a framework for the development of a rich lexical database of sign language augmented with video samples of signs. As an Open Source project, the original Signbank has formed the basis of a number of new sign language dictionaries and corpora including those for British Sign Language, Sign Language of the Netherlands and Finnish Sign Language. Versions are under development for American Sign Language and Flemish Sign Language. This paper describes the overall architecture of the Signbank system and its representation of lexical entries and associated entities.}
}

@inproceedings{kimmelman-etal-2018-ipsl:lrec,
  author    = {Kimmelman, Vadim and Klezovich, Anna and Moroz, George},
  title     = {{IPSL}: A Database of Iconicity Patterns in Sign Languages. Creation and Use},
  pages     = {4230--4234},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo,  H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu},
  booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Miyazaki, Japan},
  day       = {7--12},
  month     = may,
  year      = {2018},
  isbn      = {979-10-95546-00-9},
  language  = {english},
  url       = {https://aclanthology.org/L18-1667},
  abstract  = {We created the first large-scale database of signs annotated according to various parameters of iconicity. The signs represent concrete concepts in seven semantic fields in nineteen sign languages; 1542 signs in total. Each sign was annotated with respect to the type of form-image association, the presence of iconic location and movement, personification, and with respect to whether the sign depicts a salient part of the concept. We also created a website: https://sl-iconicity.shinyapps.io/iconicity patterns/ with several visualization tools to represent the data from the database. It is possible to visualize iconic properties of separate concepts or iconic properties of semantic fields on the map of the world, and to build graphs representing iconic patterns for selected semantic fields. A preliminary analysis of the data shows that iconicity patterns vary across semantic fields and across languages. The database and the website can be used to further study a variety of theoretical questions related to iconicity in sign languages.}
}

@inproceedings{yu-etal-2018-sign:lrec,
  author    = {Yu, Shi and Geraci, Carlo and Abner, Natasha},
  title     = {Sign Languages and the Online World Online Dictionaries {\&} Lexicostatistics},
  pages     = {4235--4240},
  editor    = {Calzolari, Nicoletta and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo,  H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu},
  booktitle = {11th International Conference on Language Resources and Evaluation ({LREC} 2018)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Miyazaki, Japan},
  day       = {7--12},
  month     = may,
  year      = {2018},
  isbn      = {979-10-95546-00-9},
  language  = {english},
  url       = {https://aclanthology.org/L18-1668},
  abstract  = {Several online dictionaries documenting the lexicon of a variety of sign languages (SLs) are now available. These are rich resources for comparative studies, but there are methodological issues that must be addressed regarding how these resources are used for research purposes. We created a web-based tool for annotating the articulatory features of signs (handshape, location, movement and orientation). Videos from online dictionaries may be embedded in the tool, providing a mechanism for large-scale theoretically-informed sign language annotation. Annotations are saved in a spreadsheet format ready for quantitative and qualitative analyses. Here, we provide proof of concept for the utility of this tool in linguistic analysis. We used the SL adaptation of the Swadesh list (Woodward, 2000) and applied lexicostatistic and phylogenetic methods to a sample of 23 SLs coded using the web-based tool; supplementary historic information was gathered from the Ethnologue of World Languages and other online sources. We report results from the comparison of all articulatory features for four Asian SLs (Chinese, Hong Kong, Taiwanese and Japanese SLs) and from the comparison of handshapes on the entire 23 language sample. Handshape analysis of the entire sample clusters all Asian SLs together, separated from the European, American, and Brazilian SLs in the sample, as historically expected. Within the Asian SL cluster, analyses also show, for example, marginal relatedness between Chinese and Hong Kong SLs.}
}

@inproceedings{cormier:16015:sign-lang:lrec,
  author    = {Cormier, Kearsy and Crasborn, Onno and Bank, Richard},
  title     = {Digging into Signs: Emerging Annotation Standards for Sign Language Corpora},
  pages     = {35--40},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2016} 7th Workshop on the Representation and Processing of Sign Languages: Corpus Mining},
  maintitle = {10th International Conference on Language Resources and Evaluation ({LREC} 2016)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Portoro{\v z}, Slovenia},
  day       = {28},
  month     = may,
  year      = {2016},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/16015.html},
  abstract  = {This paper describes the creation of annotation standards for glossing sign language corpora as part of the Digging into Signs project (2014-2015). This project was based on the annotation of two major sign language corpora, the BSL Corpus (British Sign Language) and the Corpus NGT (Sign Language of the Netherlands). The focus of the gloss annotations in these data sets was in line with the starting point of most sign language corpora: to make general corpus annotation maximally useful regardless of the particular research focus. Therefore, the joint annotation guidelines that were the output of the project focus on basic annotation of hand activity, aiming to ensure that annotations can be made in a consistent way irrespective of the particular sign language. The annotation standard provides annotators with the means to create consistent annotations for various types of signs that in turn will facilitate cross-linguistic research. At the same time, the standard includes alternative strategies for some types of signs. In this paper we outline the key features of the joint annotation conventions arising from this project, describe the arguments around providing alternative strategies in a standard, as well as discuss reliability measures and improvement to annotation tools.}
}

@inproceedings{cormier:12033:sign-lang:lrec,
  author    = {Cormier, Kearsy and Fenlon, Jordan and Johnston, Trevor and Rentelis, Ramas and Schembri, Adam and Rowley, Katherine and Adam, Robert and Woll, Bencie},
  title     = {From Corpus to Lexical Database to Online Dictionary: Issues in annotation of the {BSL} Corpus and the Development of {BSL} {SignBank}},
  pages     = {7--12},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12033.html},
  abstract  = {One requirement of a sign language corpus is that it should be machine-readable, but only a systematic approach to annotation that involves lemmatisation of the sign language glosses can make this possible at the present time. Such lemmatisation involves grouping morphological and phonological variants together into a single lemma, so that all related variants of a sign can be identified and analysed as a single sign. This lemmatisation process is made more straightforward by the existence of a comprehensive lexical database, as in the case for Australian Sign Language (Auslan). When annotation of data collected as part of the British Sign Language (BSL) Corpus Project began, no such lexical database for BSL existed. Therefore, a lemmatised BSL lexical database was created concurrently during annotation of the BSL Corpus data. As part of ongoing work by the Deafness Cognition {\&} Language Research Centre, this lexical database is being developed into an online BSL dictionary, BSL SignBank. This paper describes the adaptation of the Auslan lexical database into a BSL lexical database, and the current development of this lexical database into BSL SignBank.}
}

@inproceedings{efthimiou:12025:sign-lang:lrec,
  author    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Glauert, John and Bowden, Richard and Braffort, Annelies and Collet, Christophe and Maragos, Petros and Lefebvre-Albaret, Fran{\c c}ois},
  title     = {Sign Language technologies and resources of the {Dicta-Sign} project},
  pages     = {37--44},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12025.html},
  abstract  = {Here we present the outcomes of Dicta-Sign FP7-ICT project. Dicta-Sign researched ways to enable communication between Deaf individuals through the development of human-computer interfaces (HCI) for Deaf users, by means of Sign Language. It has researched and developed recognition and synthesis engines for sign languages (SLs) that have brought sign recognition and generation technologies significantly closer to authentic signing. In this context, Dicta-Sign has developed several technologies demonstrated via a sign language aware Web 2.0, combining work from the fields of sign language recognition, sign language animation via avatars and sign language resources and language models development, with the goal of allowing Deaf users to make, edit, and review avatar-based sign language contributions online, similar to the way people nowadays make text-based contributions on the Web.}
}

@inproceedings{matthes:12016:sign-lang:lrec,
  author    = {Matthes, Silke and Hanke, Thomas and Regen, Anja and Storz, Jakob and Worseck, Satu and Efthimiou, Eleni and Dimou, Athanasia-Lida and Braffort, Annelies and Glauert, John and Safar, Eva},
  title     = {{Dicta-Sign} -- Building a Multilingual Sign Language Corpus},
  pages     = {117--122},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12016.html},
  abstract  = {This paper presents the multilingual corpus of four European sign languages compiled in the framework of the Dicta-Sign project. Dicta-Sign researched ways to enable communication between Deaf individuals through the development of human-computer interfaces (HCI) for Deaf users, by means of sign language. Sign language resources were compiled to inform progress in the other research areas within the project, especially video recognition of signs, sign-to-sign translation, linguistic modelling, and sign generation. The aim for the corpus data collection was to achieve as high a level of naturalness as possible with semi-spontaneous utterances under lab conditions. At the same time the elicited data were supposed to be semantically close enough to be comparable both across individual informants and for all four sign languages. The sign language data were annotated using iLex and are now made available via a web portal that allows for different access options to the data.}
}

@inproceedings{buehler:10044:sign-lang:lrec,
  author    = {Buehler, Patrick and Everingham, Mark and Zisserman, Andrew},
  title     = {Exploiting signed {TV} broadcasts for automatic learning of {British} {Sign} {Language}},
  pages     = {33--40},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10044.html},
  abstract  = {In this work, we will present several contributions towards automatic recognition of BSL signs from continuous signing video sequences. Specifically, we will address 3 main points: (i) automatic detection and tracking of the hands using a generative model of the image; (ii) automatic learning of signs from TV broadcasts of single signers, using only the supervisory information available from subtitles; and (iii) discriminative signer-independent sign recognition using automatically extracted training data from a single signer. 
\par
Our source material consists of many hours of video with continuous signing and corresponding subtitles recorded from BBC digital television. This is very challenging material for a number of reasons, including self-occlusions of the signer, self-shadowing, blur due to the speed of motion, and in particular the changing background.
\par
Knowledge of the hand position and hand shape is a pre-requisite for automatic sign language recognition. We cast the problem of detecting and tracking the hands as inference in a generative model of the image, and propose a complete model which accounts for the positions and self-occlusions of the arms. Reasonable configurations are obtained by efficiently sampling from a pictorial structure proposal distribution. The results using our method exceed the state-of-the-art for the length and stability of continuous limb tracking.
\par
Previous research in sign language recognition has typically required manual training data to be generated for each sign, e.g. a signer performing each sign in controlled conditions - a time-consuming and expensive procedure. We show that for a given signer, a large number of BSL signs can be learned automatically from TV broadcasts using the supervisory information available from subtitles broadcast simultaneously with the signing. We achieve this by modelling the problem as one of multiple instance learning. In this way we are able to extract the sign of interest from hours of signing footage, despite the very weak and "noisy" supervision from the subtitles.
\par
Lastly, we will show how the automatic recognition of signs can be extended to multiple signers. Using automatically extracted examples from a single signer we train discriminative classifiers and show that these can successfully recognize signs for unseen signers. This demonstrates that our features (hand trajectory and hand shape) generalise well across different signers, despite the significant inter-personal differences in signing.}
}

@inproceedings{efthimiou:10027:sign-lang:lrec,
  author    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Glauert, John and Bowden, Richard and Braffort, Annelies and Collet, Christophe and Maragos, Petros and Goudenove, Fran{\c c}ois},
  title     = {{DICTA-SIGN}: Sign Language Recognition, Generation and Modelling with application in Deaf Communication},
  pages     = {80--83},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10027.html},
  abstract  = {Here we present the components and objectives of the EU funded project DICTA-SIGN. Dicta-Sign (http://www.dictasign.eu) is a three-year research project that involves the Institute for Language and Speech Processing, the University of Hamburg, the University of East Anglia, the University of Surrey, LIMSI/CNRS, the Universit{\'e} Paul Sabatier, the National Technical University of Athens, and WebSourd. It aims to improve the state of web-based communication for Deaf people by allowing the use of sign language in various human-computer interaction scenarios. It researches and develops recognition and synthesis engines for signed languages, aiming at a level of detail necessary for recognizing and generating authentic signing. In this context, Dicta-Sign aims at developing several technologies demonstrated via a sign language-aware Web 2.0. 
\par
Dicta-Sign supports four European sign languages: Greek. British, German, and French Sign Language and differs from previous work in that it aims to integrate tightly recognition, animation, and machine translation. All these components are informed by appropriate linguistic models from the ground up, including phonology, grammar, and non-manual features. 
\par
Expected outputs of the project include:\begin{itemize}\item A parallel multi-lingual corpus for four national sign languages - German, British, French and Greek (DGS, BSL, LSF and GSL respectively),\item A substantial multilingual dictionary of at least 1000 signs for each represented sign language,\item A continuous sign language recognition system that achieves significant improvement in terms of coverage and accuracy of sign recognition in comparison with current technology; furthermore this system will research the novel directions of multimodal sign fusion and signer adaptation,\item A language generation and synthesis component, covering in detail the role of manual, non-manual and placement within signing space,\item Annotation tools which incorporate these technologies providing access to the corpus and whose long term utility can be judged by the up-take by other sign language researchers,\item Three bidirectional integrated prototype systems which show the utility of the system components beyond the annotation tools application,\item A showcase demonstrator which exhibits how integration of the different components can support user communication needs.\end{itemize}}
}

@inproceedings{matthes:10019:sign-lang:lrec,
  author    = {Matthes, Silke and Hanke, Thomas and Storz, Jakob and Efthimiou, Eleni and Dimou, Athanasia-Lida and Karioris, Panagiotis and Braffort, Annelies and Choisier, Annick and Pelhate, Julia and Safar, Eva},
  title     = {Elicitation tasks and materials designed for {Dicta-Sign}'s multi-lingual corpus},
  pages     = {158--163},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10019.html},
  abstract  = {Within the framework of the Dicta-Sign project, parallelised sign language corpora are being compiled for four European sign languages (BSL, DGS, GSL, and LSF). The aim for the data collection was to achieve as high a level of naturalness as can be achieved with semi-spontaneous utterances under lab conditions. Therefore, informants were filmed in pairs interacting with each other. With respect to parallelisability, elicitation tasks had to be designed that result in semantically close answers without predetermining the choice of vocabulary and grammar. 
\par
The domain selected for Dicta-Sign is `Travel across Europe'. The tasks developed within the project cover different interaction formats ranging from monologues to sequences of very short turns, also with different levels of predictability. They include communication for transport by different means and contexts as well as related personal experiences. The elicitation materials are of different media formats and at various levels of complexity. They comprise of town and transportation network maps, pictures displaying a variety of places, items and situations linked to the target domain, as well as stories presented in sign language or as a picture story. In each session ten different tasks are to be performed, each of them planned to have a duration of about five to ten minutes, thereby switching roles between the informants several times during a recording session. 
\par
Taking into account cultural differences as well as language dependent issues regarding the different countries in the project, the material was designed in a way that only minor adjustments are needed that do not change the character of a task. The elicitation tasks and materials developed within the project as well as experiences gained adjusting and using the material for Dicta-Sign's different target languages are illustrated in this paper.}
}

@inproceedings{safar:10060:sign-lang:lrec,
  author    = {Safar, Eva and Glauert, John},
  title     = {Sign Language {HPSG}},
  pages     = {204--207},
  editor    = {Dreuw, Philippe and Efthimiou, Eleni and Hanke, Thomas and Johnston, Trevor and Mart{\'i}nez Ruiz, Gregorio and Schembri, Adam},
  booktitle = {Proceedings of the {LREC2010} 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies},
  maintitle = {7th International Conference on Language Resources and Evaluation ({LREC} 2010)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Valletta, Malta},
  day       = {22--23},
  month     = may,
  year      = {2010},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/10060.html},
  abstract  = {We present an overview of some relevant aspects of sign language synthesis in the ViSiCAST project, which might serve as a possible basis for the Dicta-Sign project. Dicta-Sign is a 3-year EU-funded project, that undertakes parallel corpus collection in different Sign Languages (SLs) and fundamental research and development of sign recognition and generation techniques in order to open up new potential applications for sign language users. One of the aims in Dicta-Sign is to find a model that is suitable for both recognition and generation. In this paper we revisit the main aspects of the synthesis techniques implemented in ALE Prolog using a sign language specific HPSG with the view for future changes needed. We briefly describe the HPSG feature structure and the rules and principles of the grammar, which cover important SL phenomena like mode, prodrop, plurals, classifiers and signing space.}
}

@inproceedings{schembri:08005:sign-lang:lrec,
  author    = {Schembri, Adam},
  title     = {{British} {Sign} {Language} {Corpus} Project: Open Access Archives and the Observer's Paradox},
  pages     = {165--169},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Hanke, Thomas and Thoutenhoofd, Ernst D. and Zwitserlood, Inge},
  booktitle = {Proceedings of the {LREC2008} 3rd Workshop on the Representation and Processing of Sign Languages: Construction and Exploitation of Sign Language Corpora},
  maintitle = {6th International Conference on Language Resources and Evaluation ({LREC} 2008)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marrakech, Morocco},
  day       = {1},
  month     = jun,
  year      = {2008},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/08005.html},
  abstract  = {The British Sign Language Corpus Project is a new three-year project (2008-2010) that aims to create a machine-readable digital corpus of spontaneous and elicited British Sign Language (BSL) collected from deaf native signers and early learners across the United Kingdom. In the field of sign language studies, it represents a unique combination of methodology from variationist sociolinguistics and corpus linguistics. The project aims to conduct a studies of sociolinguistic variation, language change and language contact simultaneously with the creation of a corpus. As such the nature of the dataset to be collected will be guided by the need to create a judgement sample of the deaf community rather than a strictly representative sample. Although the recruitment of participants will be balanced for gender and age, it will focus only on signers exposed to BSL before the age of 7 years, and adult deaf native signers will be disproportionately represented. Signers will also be filmed in 8 key regions across the United Kingdom, with a minimum of 30 participants from each region. Furthermore, participant recruitment will rely on deaf community fieldworkers in each region, using a technique of `network sampling' in which the local community member begins by recruiting people he or she knows, and asks these individuals to recommend other individuals matching the project criteria. Moreover, the data will be limited in terms of situational varieties, focusing mainly on conversational and interview data, together with narratives and some elicitation tasks. Unlike previous large-scale sociolinguistic projects, however, the dataset will be partly annotated and tagged using ELAN software, given metadata descriptions using IMDI tools, and will be archived and made accessible and searchable on-line. As such, we hope that it will become a standard reference and core data source for all researchers investigating BSL structure and use. This means, however, that, unlike previous sociolinguistic projects on ASL and Auslan, participants must consent to having the video data of their sign language use made public. This seems to put at risk the authenticity of the data collected, as signers may monitor their production more carefully than might otherwise occur. As the aim of variationist sociolinguistics is to study the vernacular variety (i.e., the variety adopted by speakers/signers when they are monitoring their style least closely), open-access archives thus may not always provide the best data source. While recognising that this concept of the vernacular represents an abstraction, we discuss the possibility of overcoming this problem by making some of the conversational data password protected for use by academic researchers only, while making other parts of the corpus publicly available as part of a dual access archive of BSL.}
}

@inproceedings{zahedi:06005:sign-lang:lrec,
  author    = {Zahedi, Morteza and Dreuw, Philippe and Rybach, David and Deselaers, Thomas and Bungeroth, Jan and Ney, Hermann},
  title     = {Continuous Sign Language Recognition -- Approaches from Speech Recognition and Available Data Resources},
  pages     = {21--24},
  editor    = {Vettori, Chiara},
  booktitle = {Proceedings of the {LREC2006} 2nd Workshop on the Representation and Processing of Sign Languages: Lexicographic Matters and Didactic Scenarios},
  maintitle = {5th International Conference on Language Resources and Evaluation ({LREC} 2006)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Genoa, Italy},
  day       = {28},
  month     = may,
  year      = {2006},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/06005.html},
  abstract  = {In this paper we describe our current work on automatic continuous sign language recognition. We present an automatic sign language recognition system that is based on a large vocabulary speech recognition system and adopts many of the approaches that are conven- tionally applied in the recognition of spoken language. Furthermore, we present a set of freely available databases that can be used for training, testing and performance evaluation of sign language recognition systems. First results on one of the databases are given, we show that the approaches from spoken language recognition are suitable, and we give directions for further research.}
}

@inproceedings{mertzani:06013:sign-lang:lrec,
  author    = {Mertzani, Maria},
  title     = {Sign Language Learning through Asynchronous Computer Mediated Communication},
  pages     = {64--69},
  editor    = {Vettori, Chiara},
  booktitle = {Proceedings of the {LREC2006} 2nd Workshop on the Representation and Processing of Sign Languages: Lexicographic Matters and Didactic Scenarios},
  maintitle = {5th International Conference on Language Resources and Evaluation ({LREC} 2006)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Genoa, Italy},
  day       = {28},
  month     = may,
  year      = {2006},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/06013.html},
  abstract  = {Current research shows that CMC provides an excellent vehicle for L2 learning since it affords both teachers and learners to communicate in an authentic learning environment where negotiation of meaning in the target language can take place in the same way as in face-to-face interaction. As bandwidth networks become more developed, it is feasible to transmit sign language communication using digitised video. In this paper, I present SignLab, a virtual sign laboratory at the Centre for Deaf Studies (CDS), in Bristol University, U.K., developed through the use of `Panda' software. It is an asynchronous videoconferencing system developed for the learning of British Sign Language. In this paper, I discuss how SignLab changes the concept of traditional sign language teaching and learning in terms of course delivery, tutors' and students' online roles, course material and online communication and collaboration. At the end, I propose a framework based on constructivist and learner-centred principles that teachers may consider applying when teaching online.}
}

@inproceedings{mertzani:06018:sign-lang:lrec,
  author    = {Mertzani, Maria and Denmark, Clark and Day, Linda},
  title     = {Forming Sign Language Learning Environments in Cyberspace},
  pages     = {88--91},
  editor    = {Vettori, Chiara},
  booktitle = {Proceedings of the {LREC2006} 2nd Workshop on the Representation and Processing of Sign Languages: Lexicographic Matters and Didactic Scenarios},
  maintitle = {5th International Conference on Language Resources and Evaluation ({LREC} 2006)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Genoa, Italy},
  day       = {28},
  month     = may,
  year      = {2006},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/06018.html},
  abstract  = {In this paper we would like to present the way virtual learning environments (VLEs) are employed into the teaching and learning of British Sign Language (BSL) at the Centre for Deaf Studies of Bristol University, U.K. By considering cyberspace a culturally constructed environment where people can form different virtual communities, this paper will focus on the creation of a virtual learning community for the purposes of BSL learning. Both tutors and students have access and meet on two main websites: SignStation and DeafStation, from where they can retrieve authentic BSL material during their classes and interact through a videoconferencing software system, Panda. We describe the development of VLE and discuss the practices employed when meeting online in terms of instruction delivery and knowledge construction.}
}

@inproceedings{crasborn:04005:sign-lang:lrec,
  author    = {Crasborn, Onno and van der Kooij, Els and Broeder, Daan and Brugman, Hennie},
  title     = {Sharing sign language corpora online: proposals for transcription and metadata categories},
  pages     = {20--23},
  editor    = {Streiter, Oliver and Vettori, Chiara},
  booktitle = {Proceedings of the {LREC2004} Workshop on the Representation and Processing of Sign Languages: From {SignWriting} to Image Processing. Information techniques and their implications for teaching, documentation and communication},
  maintitle = {4th International Conference on Language Resources and Evaluation ({LREC} 2004)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Lisbon, Portugal},
  day       = {30},
  month     = may,
  year      = {2004},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/04005.html},
  abstract  = {This paper presents the results of a European project called ECHO, which included an effort to publish sign language corpora online. The aim of the ECHO project was to explore the intricacies of sharing data using the internet in all areas of the humanities. For sign language, this involved adding a specific profile to the IMDI metadata set for characterizing spoken language corpora, and developing a set of transcription conventions that are useful for a broad audience of linguists. In addition to presenting these results, we outline some options for future technological developments, and bring forward some ethical problems relating to publishing video data on internet.}
}