{"id":57066,"date":"2026-06-12T11:24:50","date_gmt":"2026-06-12T15:24:50","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine\/?p=57066"},"modified":"2026-06-12T11:24:50","modified_gmt":"2026-06-12T15:24:50","slug":"handshaping-communication","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine\/inquiring-minds\/handshaping-communication\/","title":{"rendered":"Handshaping\u00a0Communication"},"content":{"rendered":"\n

Handshapes\u2014distinctive hand configurations used to form signs\u2014are the building blocks of any sign language. But despite their linguistic importance, many AI models for processing sign language rarely model them explicitly. <\/p>\n\n\n\n

Among the challenges in AI hand shape recognition are that the same handshape can appear in different configurations in one video, and a single sign can comprise multiple handshapes. <\/p>\n\n\n

\n
\"Portrait
Alessa Carbo<\/figcaption><\/figure>\n<\/div>\n\n\n

Alessa Carbo, a third-year computer science student working with Eric Nalisnick<\/a>, an assistant professor of computer science<\/a>, has taken an important step toward addressing this challenge. She and Nalisnick developed a neural network to improve the computational recognition and translation of signed languages; Carbo is first author of their findings, which were published in the Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing<\/a><\/em>. <\/p>\n\n\n\n

Their approach could give researchers new insights into how people use sign languages and help pave the way for enhanced linguistic analysis and better communication technologies for the Deaf and hard-of-hearing communities. <\/p>\n\n\n\n

Johns Hopkins computer scientists previously demonstrated that modeling handshapes can improve American Sign Language translation accuracy by 15%. Carbo and Nalisnick wanted to see if they could improve on that by separating a hand\u2019s static orientation from a signer\u2019s motions over time. <\/p>\n\n\n\n

To do this, they used a graph neural network divided into two submodels: One looks at how the hand\u2019s shape changes over time during a single sign, while the other searches for video frames that show a handshape in its most recognizable orientation. Testing this approach on a new dataset of annotated ASL videos, they found that separating time dynamics and hand configuration improves handshape recognition significantly. <\/p>\n\n\n\n

\u201cAnd because handshape is a\u00a0fundamental parameter of all\u00a0sign languages, our model can\u00a0be a useful tool for sign language\u00a0linguists,\u201d Carbo says. \u201cSay a\u00a0linguist has a large collection\u00a0of videos of people signing and\u00a0wants to find all occurrences\u00a0of a particular handshape\u2014our model could allow her\u00a0to quickly and automatically\u00a0find those video segments.\u00a0<\/p>\n\n\n\n

\u201cWe\u2019re excited that our work could help with both AI applications and researchers trying to find new insights about how people use sign languages around the globe,\u201d Carbo says. <\/p>\n\n\n\n

\u2014 JAIMIE PATTERSON <\/p>\n","protected":false},"excerpt":{"rendered":"

Neural network
\nimproves the computational
\nrecognition and translation of
\nsigned languages.<\/p>\n","protected":false},"author":5,"featured_media":57069,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[32],"tags":[],"issue":[105],"class_list":["post-57066","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-inquiring-minds","issue-spring-2026"],"acf":[],"yoast_head":"\nHandshaping\u00a0Communication - Johns Hopkins Engineering Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine\/inquiring-minds\/handshaping-communication\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Handshaping\u00a0Communication - Johns Hopkins Engineering Magazine\" \/>\n<meta property=\"og:description\" content=\"Neural network improves the computational recognition and translation of signed languages.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/magazine\/inquiring-minds\/handshaping-communication\/\" \/>\n<meta property=\"og:site_name\" content=\"Johns Hopkins Engineering Magazine\" \/>\n<meta property=\"article:published_time\" content=\"2026-06-12T15:24:50+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/engineering.jhu.edu\/magazine\/wp-content\/uploads\/2026\/06\/JHU7032-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"1707\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Monica Leigh\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/inquiring-minds\\\/handshaping-communication\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/inquiring-minds\\\/handshaping-communication\\\/\"},\"author\":{\"name\":\"Monica Leigh\",\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/#\\\/schema\\\/person\\\/19b4705df419225ddfc32b0473d2e58f\"},\"headline\":\"Handshaping\u00a0Communication\",\"datePublished\":\"2026-06-12T15:24:50+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/inquiring-minds\\\/handshaping-communication\\\/\"},\"wordCount\":434,\"publisher\":{\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/inquiring-minds\\\/handshaping-communication\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/wp-content\\\/uploads\\\/2026\\\/06\\\/JHU7032-scaled.jpg\",\"articleSection\":[\"Inquiring Minds: Student Discovery\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/inquiring-minds\\\/handshaping-communication\\\/\",\"url\":\"https:\\\/\\\/engineering.jhu.edu\\\/magazine\\\/inquiring-minds\\\/handshaping-communication\\\/\",\"name\":\"Handshaping\u00a0Communication - 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