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  <doc>
    <id>1767</id>
    <completedYear>2023</completedYear>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>20</pageNumber>
    <edition/>
    <issue/>
    <volume>81</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-04-21</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Classification of Stuttering – The ComParE challenge and beyond</title>
    <abstract language="eng">The ACM Multimedia 2022 Computational Paralinguistics Challenge (ComParE) featured a sub-challenge on the classification of stuttering in order to bring attention to this important topic and engage a wider research community. Stuttering is a complex speech disorder characterized by blocks, prolongations of sounds and syllables, and repetitions of sounds and words. Accurately classifying the symptoms of stuttering has implications for the development of self-help tools and specialized automatic speech recognition systems (ASR) that can handle atypical speech patterns. This paper provides a review of the challenge contributions and improves upon them with new state-of-the-art classification results for the KSF-C dataset, and explores cross-language training to demonstrate the potential of datasets in multiple languages. To facilitate further research and reproducibility, the full KSF-C dataset, including test-set labels, is also released.</abstract>
    <parentTitle language="eng">Computer Speech &amp; Language</parentTitle>
    <identifier type="doi">10.1016/j.csl.2023.101519</identifier>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.import.date">2024-06-27T13:39:31+00:00</enrichment>
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    <licence>Keine Lizenz - Deutsches Urheberrecht gilt</licence>
    <author>Sebastian P. Bayerl</author>
    <author>Maurice Gerczuk</author>
    <author>Anton Batliner</author>
    <author>Christian Bergler</author>
    <author>Shahin Amiriparian</author>
    <author>Björn Schuller</author>
    <author>Elmar Nöth</author>
    <author>Korbinian Riedhammer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dysfluency Stuttering ComParE challenge Paralinguistics Pathological speech</value>
    </subject>
    <collection role="institutes" number="">Fakultät Informatik</collection>
  </doc>
  <doc>
    <id>1787</id>
    <completedYear>2022</completedYear>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7120-7124</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-10-10</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The ACM Multimedia 2022 Computational Paralinguistics Challenge</title>
    <abstract language="eng">The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComParE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectrum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN.</abstract>
    <subTitle language="eng">Vocalisations, Stuttering, Activity, &amp; Mosquitoes</subTitle>
    <identifier type="doi">10.1145/3503161.3551591</identifier>
    <enrichment key="ConferenceStatement">ACM International Conference on Multimedia</enrichment>
    <enrichment key="Reviewstatus">Begutachtet/Reviewed</enrichment>
    <enrichment key="opus.import.date">2024-06-27T13:39:44+00:00</enrichment>
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    <licence>Keine Lizenz - Deutsches Urheberrecht gilt</licence>
    <author>Björn Schuller</author>
    <author>Anton Batliner</author>
    <author>Shahin Amiriparian</author>
    <author>Christian Bergler</author>
    <author>Maurice Gerczuk</author>
    <author>Natalie Holz</author>
    <author>Pauline Larrouy-Maestri</author>
    <author>Sebastian P. Bayerl</author>
    <author>Korbinian Riedhammer</author>
    <author>Adria Mallol-Ragolta</author>
    <author>Maria Pateraki</author>
    <author>Harry Coppock</author>
    <author>Ivan Kiskin</author>
    <author>Marianne Sinka</author>
    <author>Stephen Roberts</author>
    <collection role="institutes" number="">Fakultät Informatik</collection>
  </doc>
</export-example>
