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Full-Text Articles in Sign Languages
Exploring Strategies For Modeling Sign Language Phonology, Lee Kezar, Riley Carlin, Tejas Srinivasan, Zed Sehyr, Naomi Caselli, Jesse Thomason
Exploring Strategies For Modeling Sign Language Phonology, Lee Kezar, Riley Carlin, Tejas Srinivasan, Zed Sehyr, Naomi Caselli, Jesse Thomason
Communication Sciences and Disorders Faculty Articles and Research
Like speech, signs are composed of discrete, recombinable features called phonemes. Prior work shows that models which can recognize phonemes are better at sign recognition, motivating deeper exploration into strategies for modeling sign language phonemes. In this work, we learn graph convolution networks to recognize the sixteen phoneme “types” found in ASL-LEX 2.0. Specifically, we explore how learning strategies like multi-task and curriculum learning can leverage mutually useful information between phoneme types to facilitate better modeling of sign language phonemes. Results on the Sem-Lex Benchmark show that curriculum learning yields an average accuracy of 87% across all phoneme types, outperforming …
Improving Sign Recognition With Phonology, Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr
Improving Sign Recognition With Phonology, Lee Kezar, Jesse Thomason, Zed Sevcikova Sehyr
Communication Sciences and Disorders Faculty Articles and Research
We use insights from research on American Sign Language (ASL) phonology to train models for isolated sign language recognition (ISLR), a step towards automatic sign language understanding. Our key insight is to explicitly recognize the role of phonology in sign production to achieve more accurate ISLR than existing work which does not consider sign language phonology. We train ISLR models that take in pose estimations of a signer producing a single sign to predict not only the sign but additionally its phonological characteristics, such as the handshape. These auxiliary predictions lead to a nearly 9% absolute gain in sign recognition …
An Interactive Visual Database For American Sign Language Reveals How Signs Are Organized In The Mind, Zed Sevcikova Sehyr, Ariel Goldberg, Karen Emmory, Naomi Caselli
An Interactive Visual Database For American Sign Language Reveals How Signs Are Organized In The Mind, Zed Sevcikova Sehyr, Ariel Goldberg, Karen Emmory, Naomi Caselli
Communication Sciences and Disorders Faculty Articles and Research
"We are four researchers who study psycholinguistics, linguistics, neuroscience and deaf education. Our team of deaf and hearing scientists worked with a group of software engineers to create the ASL-LEX database that anyone can use for free. We cataloged information on nearly 3,000 signs and built a visual, searchable and interactive database that allows scientists and linguists to work with ASL in entirely new ways."
The Asl-Lex 2.0 Project: A Database Of Lexical And Phonological Properties For 2,723 Signs In American Sign Language, Zed Sevcikova Sehyr, Naomi Caselli, Ariel M. Cohen-Goldberg, Karen Emmory
The Asl-Lex 2.0 Project: A Database Of Lexical And Phonological Properties For 2,723 Signs In American Sign Language, Zed Sevcikova Sehyr, Naomi Caselli, Ariel M. Cohen-Goldberg, Karen Emmory
Communication Sciences and Disorders Faculty Articles and Research
ASL-LEX is a publicly available, large-scale lexical database for American Sign Language (ASL). We report on the expanded database (ASL-LEX 2.0) that contains 2,723 ASL signs. For each sign, ASL-LEX now includes a more detailed phonological description, phonological density and complexity measures, frequency ratings (from deaf signers), iconicity ratings (from hearing non-signers and deaf signers), transparency (“guessability”) ratings (from non-signers), sign and videoclip durations, lexical class, and more. We document the steps used to create ASL-LEX 2.0 and describe the distributional characteristics for sign properties across the lexicon and examine the relationships among lexical and phonological properties of signs. Correlation …