Papers with LSF

7 papers
A Low-Cost Motion Capture Corpus in French Sign Language for Interpreting Iconicity and Spatial Referencing Mechanisms (2022.lrec-1)

Copied to clipboard

Challenge: Existing tools for automatic translation of sign language videos into transcribed texts are limited.
Approach: They propose to use deep learning methods to circumvent the use of models in spatial referencing recognition by a 3D skeleton and a software program to capture and post-process the LSF-SHELVES corpus.
Outcome: The proposed system targets iconicity and spatial referencing in french sign language . it is light-weight and low-cost to collect data from a large panel of signers .
Multidimensional Coding of Multimodal Languaging in Multi-Party Settings (2022.lrec-1)

Copied to clipboard

Challenge: In natural language settings, many interactions include more than two speakers and real-life interpretation is based on all types of information available in all modalities.
Approach: They propose to use a coding tool to analyze spontaneous interactions in family dinner settings.
Outcome: The proposed method compares the language of two adults and three children in family dinner settings using either French, or French sign language.
Rosetta-LSF: an Aligned Corpus of French Sign Language and French for Text-to-Sign Translation (2022.lrec-1)

Copied to clipboard

Challenge: a new corpus of french Sign Language (LSF) data is created to support future studies on the automatic translation of written French into LSF, rendered through the animation of a virtual signer.
Approach: They propose to use a French Sign Language corpus called "Rosetta-LSF" it is intended to support studies on automatic translation of written French into LSF .
Outcome: The proposed corpus supports future studies on automatic translation of written French into LSF, rendered through animation of a virtual signer.
Extending AZee with Non-manual Gesture Rules for French Sign Language (2024.lrec-main)

Copied to clipboard

Challenge: Currently, Sign Languages (SLs) are under-resourced and are difficult to develop.
Approach: They propose to extend AZee to formally represent Sign Language discourses, but also to animate them with a virtual signer.
Outcome: The proposed model allows to formally represent Sign Language discourses, but also to animate them with a virtual signer.
Elicitation protocol and material for a corpus of long prepared monologues in Sign Language (L18-1)

Copied to clipboard

Challenge: elicitation of long discourses is difficult in Sign Language, and is often a problem . e.g., elicitation of long texts is a technique that can be used to collect long discourse .
Approach: They propose a protocol and two tasks to collect long discourse in Sign Language . they propose to ensure both are collected and prepared in the language .
Outcome: The proposed protocol improves the produced data and the results of a test with LSF informants.
Modeling French Sign Language: a proposal for a semantically compositional system (L18-1)

Copied to clipboard

Challenge: Several studies have proposed linguistic models to describe sign languages, but none have succeeded to describe the specificities of SL.
Approach: They propose a linguistic approach to formalize the sign language (SL) they propose to take into account linguistic properties of the SL while respecting constraints of a modelisation process.
Outcome: The proposed model takes into account linguistic properties of the sign language while respecting constraints of a modelisation process.
LSF-ANIMAL: A Motion Capture Corpus in French Sign Language Designed for the Animation of Signing Avatars (2020.lrec-1)

Copied to clipboard

Challenge: Signing avatars are often procedurally animated, resulting in robotic and unnatural movements, which are therefore rejected by the Deaf community.
Approach: They propose to use a French Sign Language corpus to create an avatar that can be edited from motion capture data to create new signs and utterances.
Outcome: The proposed corpus is based on a french Sign Language (LSF) corpus composed of captured signs and sentences.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations