Heterogeneous Data Sources for Signed Language Analysis and Synthesis: The SignCom Project

8094

Authors

nan

Abstract

AbstractThis paper describes how heterogeneous data sources captured in the SignCom project may be used for the analysis and synthesis of French Sign Language (LSF) utterances. The captured data combine video data and multimodal motion capture (mocap) data, including body and hand movements as well as facial expressions. These data are pre-processed, synchronized, and enriched by text annotations of signed language elicitation sessions. The addition of mocap data to traditional data structures provides additional phonetic data to linguists who desire to better understand the various parts of signs (handshape, movement, orientation, etc.) to very exacting levels, as well as their interactions and relative timings. We show how the phonologies of hand configurations and articulator movements may be studied using signal processing and statistical analysis tools to highlight regularities or temporal schemata between the different modalities. Finally, mocap data allows us to replay signs using a computer animation engine, specifically editing and rearranging movements and configurations in order to create novel utterances.

Keywords

nan

Full Content

nan

Labeled Text

AbstractThis paper describes how heterogeneous data sources captured in the SignCom project may be used for the analysis and synthesis of French Sign Language (LSF) utterances. The captured data combine video data and multimodal motion capture (mocap) data, including body and hand movements as well as facial expressions. These data are pre-processed, synchronized, and enriched by text annotations of signed language elicitation sessions. The addition of mocap data to traditional data structures provides additional phonetic data to linguists who desire to better understand the various parts of signs (handshape, movement, orientation, etc.) to very exacting levels, as well as their interactions and relative timings. We show how the phonologies of hand configurations and articulator movements may be studied using signal processing and statistical analysis tools to highlight regularities or temporal schemata between the different modalities. Finally, mocap data allows us to replay signs using a computer animation engine, specifically editing and rearranging movements and configurations in order to create novel utterances.

Publication Details

Paper ID:
8094
Year:
2010
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan