GLips - German Lipreading Dataset
- Link:
- Autor/in:
- Verlag/Körperschaft:
- Universität Hamburg
- Erscheinungsjahr:
- 2022
- Medientyp:
- Datensatz
- Schlagworte:
-
- Computer Vision
- Pattern Recognition
- Machine Learning
- Deep Learning
- Language
- Dataset
- Automatic Speech Recognition
- Transfer Learning
- Lip Reading
- Corpus
- Beschreibung:
-
The German Lipreading dataset consists of 250,000 publicly available videos of the faces of speakers of the Hessian Parliament, which was processed for word-level lip reading using an automatic pipeline. The format is similar to that of the English language Lip Reading in the Wild (LRW) dataset, with each H264-compressed MPEG-4 video encoding one word of interest in a context of 1.16 seconds duration, which yields compatibility for studying transfer learning between both datasets. Choosing video material based on naturally spoken language in a natural environment ensures more robust results for real-world applications than artificially generated datasets with as little noise as possible. The 500 different spoken words ranging between 4-18 characters in length each have 500 instances and separate MPEG-4 audio- and text metadata-files, originating from 1018 parliamentary sessions. Additionally, the complete TextGrid files containing the segmentation information of those sessions are also included. The size of the uncompressed dataset is 16GB.
- Copyright of original data: Hessian Parliament (https://hessischer-landtag.de). If you use this dataset, you agree to use it for research purpose only and to cite the following reference in any works that make any use of the dataset. Reference: Gerald Schwiebert, Cornelius Weber, Leyuan Qu, Henrique Siqueira, Stefan Wermter (2022). A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning. arXiv:2202.13403
- {"references": ["Gerald Schwiebert, Cornelius Weber, Leyuan Qu, Henrique Siqueira, Stefan Wermter (2022). A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning", "arXiv:2202.13403"]}
- Lizenzen:
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- https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
- info:eu-repo/semantics/openAccess
- Quellsystem:
- Forschungsdatenrepositorium der UHH
Interne Metadaten
- Quelldatensatz
- oai:fdr.uni-hamburg.de:10048