{"id":599343,"date":"2019-07-23T11:12:46","date_gmt":"2019-07-23T18:12:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=599343"},"modified":"2021-01-20T11:55:25","modified_gmt":"2021-01-20T19:55:25","slug":"sign-language-recognition-generation-and-translation-an-interdisciplinary-perspective","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/sign-language-recognition-generation-and-translation-an-interdisciplinary-perspective\/","title":{"rendered":"Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective"},"content":{"rendered":"<p>Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of  fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. This leads to three key questions: 1) What does an interdisciplinary view of the current landscape reveal? 2) What are the biggest challenges facing the field? and 3) What are the calls to action for people working in the field? To help answer these questions, we brought together a diverse group of experts for a two-day workshop. This paper presents the results of that interdisciplinary workshop, providing key background that is often overlooked by computer scientists, a review of the state-of-the-art, a set of pressing challenges, and a call to action for the research community.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in separate disciplinary silos, and tackles separate portions of the sign language processing pipeline. [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"ASSETS 2019","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2019-10-28","msr_highlight_text":"","msr_notes":"Best Paper Award","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13562,13545,13554],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-599343","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computer-vision","msr-research-area-human-language-technologies","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-10-28","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"Best Paper Award","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/07\/Sign_Language_Workshop_Camera2_accessible.pdf","id":"605577","title":"sign_language_workshop_camera2_accessible","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":605577,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/08\/Sign_Language_Workshop_Camera2_accessible.pdf"},{"id":602991,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/08\/Sign_Language_Workshop_Camera_accessible.pdf"},{"id":599346,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/07\/Sign_Language_Workshop_accessible.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Danielle Bragg","user_id":37592,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Danielle Bragg"},{"type":"user_nicename","value":"Oscar Koller","user_id":38493,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Oscar Koller"},{"type":"text","value":"Mary Bellard","user_id":0,"rest_url":false},{"type":"text","value":"Larwan Berke","user_id":0,"rest_url":false},{"type":"text","value":"Patrick Boudrealt","user_id":0,"rest_url":false},{"type":"text","value":"Annelies Braffort","user_id":0,"rest_url":false},{"type":"text","value":"Naomi Caselli","user_id":0,"rest_url":false},{"type":"text","value":"Matt Huenerfauth","user_id":0,"rest_url":false},{"type":"text","value":"Hernisa Kacorri","user_id":0,"rest_url":false},{"type":"text","value":"Tessa Verhoef","user_id":0,"rest_url":false},{"type":"text","value":"Christian Vogler","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Meredith Ringel Morris","user_id":32884,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Meredith Ringel Morris"}],"msr_impact_theme":[],"msr_research_lab":[199563,199565],"msr_event":[559521],"msr_group":[283244,371909],"msr_project":[614286,638724],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":614286,"post_title":"Data-Driven Accessibility Systems","post_name":"data-driven-accessibility-systems","post_type":"msr-project","post_date":"2019-10-28 09:08:40","post_modified":"2023-11-14 18:22:49","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/data-driven-accessibility-systems\/","post_excerpt":"Microsoft Research New England aims to build the accessibility systems of the future. In particular, we are focused on building systems to better support sign language users and low-vision readers. Accessibility is a major concern for many people with disabilities. Over a billion people worldwide (and nearly one in five in the U.S.1) live with some form of disability. Despite this large group, which is growing as the world\u2019s population ages, many technical systems are&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/614286"}]}},{"ID":638724,"post_title":"AI Fairness and Disability","post_name":"ai-fairness-and-disability","post_type":"msr-project","post_date":"2020-02-22 16:11:34","post_modified":"2021-01-20 17:37:14","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/ai-fairness-and-disability\/","post_excerpt":"This project examines issues around AI FATE (Fairness, Accountability, Transparency, and Ethics) and Responsible AI, with a particular focus on how AI systems impact people with disabilities. This includes ensuring that mainstream AI tools are inclusively designed such that they work correctly for users regardless of ability status, that accessibility-oriented AI technologies are designed in a human-centered way to solve problems that truly matter to end-users, and that AI practitioners follow inclusive design processes when&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/638724"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/599343","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/599343\/revisions"}],"predecessor-version":[{"id":599349,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/599343\/revisions\/599349"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=599343"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=599343"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=599343"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=599343"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=599343"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=599343"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=599343"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=599343"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=599343"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=599343"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=599343"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=599343"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=599343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}