Name
Abstract | ||
|---|---|---|
Sign language recognition (SLR) involves identifying the form and meaning of isolated signs or sequences of signs. To our knowledge,the combination of SLR and sign language assessment is novel. The goal of an ongoing three-year project in Switzerland is to pioneeran assessment system for lexical signs of Swiss German Sign Language (Deutschschweizerische Geb¨ardensprache, DSGS) that relies onSLR. The assessment system aims to give adult L2 learners of DSGS feedback on the correctness of the manual parameters (handshape,hand position, location, and movement) of isolated signs they produce. In its initial version, the system will include automatic feedbackfor a subset of a DSGS vocabulary production test consisting of 100 lexical items. To provide the SLR component of the assessmentsystem with sufficient training samples, a large-scale dataset containing videotaped repeated productions of the 100 items of thevocabulary test with associated transcriptions and annotations was created, consisting of data from 11 adult L1 signers and 19 adult L2learners of DSGS. This paper introduces the dataset, which will be made available to the research community. |
Year | Venue | Field |
|---|---|---|
2018 | LREC | Transcription (linguistics),Computer science,Lexical item,Correctness,Handshape,Sign language,Artificial intelligence,Natural language processing,Vocabulary,Swiss-German sign language |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| sarah ebling | 1 | 23 | 3.95 |
| Necati Cihan Camgöz | 2 | 39 | 9.23 |
| Penny Boyes Braem | 3 | 1 | 0.35 |
| Katja Tissi | 4 | 1 | 0.35 |
| Sandra Sidler-Miserez | 5 | 1 | 0.35 |
| Stephanie Stoll | 6 | 6 | 1.45 |
| Simon Hadfield | 7 | 356 | 22.65 |
| Tobias Haug | 8 | 1 | 0.68 |
| Richard Bowden | 9 | 1840 | 118.50 |
| Sandrine Tornay | 10 | 1 | 1.02 |
| Marzieh Razavi | 11 | 30 | 4.12 |
| Mathew Magimai-Doss | 12 | 516 | 54.76 |