English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

Application of audio and video processing methods for language research

MPS-Authors
/persons/resource/persons4464

Lenkiewicz,  Przemyslaw
The Language Archive, MPI for Psycholinguistics, Max Planck Society;

/persons/resource/persons216

Wittenburg,  Peter
The Language Archive, MPI for Psycholinguistics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
Supplementary Material (public)
There is no public supplementary material available
Citation

Lenkiewicz, P., Wittenburg, P., Schreer, O., Masneri, S., Schneider, D., & Tschöpel, S. (2011). Application of audio and video processing methods for language research. In Proceedings of the conference Supporting Digital Humanities 2011 [SDH 2011], Copenhagen, Denmark, November 17-18, 2011.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0012-5C0D-0
Abstract
Annotations of media recordings are the grounds for linguistic research. Since creating those annotations is a very laborious task, reaching 100 times longer than the length of the annotated media, innovative audio and video processing algorithms are needed, in order to improve the efficiency and quality of annotation process. The AVATecH project, started by the Max-Planck Institute for Psycholinguistics (MPI) and the Fraunhofer institutes HHI and IAIS, aims at significantly speeding up the process of creating annotations of audio-visual data for humanities research. In order for this to be achieved a range of state-of-the-art audio and video pattern recognition algorithms have been developed and integrated into widely used ELAN annotation tool. To address the problem of heterogeneous annotation tasks and recordings we provide modular components extended by adaptation and feedback mechanisms to achieve competitive annotation quality within significantly less annotation time.