March 1, 2022 Dataset Open Access

Schwiebert, Gerald; Weber, Cornelius; Qu, Leyuan; Siqueira, Henrique; Wermter, Stefan

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  <identifier identifierType="DOI">10.25592/uhhfdm.10048</identifier>
  <creators>
    <creator>
      <creatorName>Schwiebert, Gerald</creatorName>
      <affiliation>University of Hamburg</affiliation>
    </creator>
    <creator>
      <creatorName>Weber, Cornelius</creatorName>
      <affiliation>University of Hamburg</affiliation>
    </creator>
    <creator>
      <creatorName>Qu, Leyuan</creatorName>
      <affiliation>University of Hamburg</affiliation>
    </creator>
    <creator>
      <creatorName>Siqueira, Henrique</creatorName>
      <affiliation>University of Hamburg</affiliation>
    </creator>
    <creator>
      <creatorName>Wermter, Stefan</creatorName>
      <affiliation>University of Hamburg</affiliation>
    </creator>
  </creators>
  <titles>
    <title>GLips - German Lipreading Dataset</title>
  </titles>
  <publisher>Universität Hamburg</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>Computer Vision</subject>
    <subject>Pattern Recognition</subject>
    <subject>Machine Learning</subject>
    <subject>Deep Learning</subject>
    <subject>Language</subject>
    <subject>Dataset</subject>
    <subject>Automatic Speech Recognition</subject>
    <subject>Transfer Learning</subject>
    <subject>Lip Reading</subject>
    <subject>Corpus</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-03-01</date>
  </dates>
  <language>de</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://www.fdr.uni-hamburg.de/record/10048</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="arXiv" relationType="IsReferencedBy">arXiv:2202.13403</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsPartOf">10.25592/uhhfdm.10047</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
    <description descriptionType="Other">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</description>
    <description descriptionType="Other">{"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"]}</description>
  </descriptions>
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