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    <completedDate>2021-12-20</completedDate>
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    <title language="eng">Autoencoder-based Ultrasonic NDT of Adhesive Bonds</title>
    <abstract language="eng">We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with autoencoders.The models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added defects.The resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.</abstract>
    <parentTitle language="eng">IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia</parentTitle>
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    <identifier type="doi">10.1109/SENSORS47087.2021.9639864</identifier>
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      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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    <author>
      <firstName>Martin</firstName>
      <lastName>Barth</lastName>
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      <firstName>Constanze</firstName>
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    <title language="eng">Paper Tissue Softness Rating by Acoustic Emission Analysis</title>
    <abstract language="eng">Softness is one of the essential properties of hygiene tissue products. Reliably measuring it is of utmost importance to ensure the balance between customer expectations and cost-effective tissue production. This study presents a method for assessing softness by analyzing acoustic emissions produced while tearing a tissue specimen. The aim was to train neural network models using the corrected results of human panel tests as the ground truth labels and to predict the tissue softness in two- and three-class recognition tasks. We also investigate the possibility of predicting some production parameters related to the softness property. The results proved that tissue softness and production parameters could be reliably estimated only by the tearing noise.</abstract>
    <parentTitle language="eng">Applied Sciences</parentTitle>
    <identifier type="issn">2076-3417</identifier>
    <identifier type="doi">10.3390/app13031670</identifier>
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    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Schubert</lastName>
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    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>acoustic emission</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tissue softness analysis</value>
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    <publishedYear>2012</publishedYear>
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    <language>deu</language>
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    <publisherName>DGZfP</publisherName>
    <publisherPlace>Berlin</publisherPlace>
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    <contributingCorporation>Fraunhofer IZFP Dresden, TU Dresden</contributingCorporation>
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    <completedDate>2013-03-26</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="deu">Automatisierte Weichheitsprüfung von Papier</title>
    <parentTitle language="deu">ZfP in Forschung, Entwicklung und Anwendung, Graz, 17. - 19. September 2012, DACH-Jahrestagung 2012 Zerstörungsfreie Materialprüfung</parentTitle>
    <identifier type="isbn">978-394-02834-4-3</identifier>
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    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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    <submitter>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </submitter>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <author>
      <firstName>Guntram</firstName>
      <lastName>Strecha</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Thomas</firstName>
      <lastName>Feher</lastName>
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    <author>
      <firstName>Rüdiger</firstName>
      <lastName>Hoffmann</lastName>
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    <publishedYear>2010</publishedYear>
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    <language>deu</language>
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    <type>report</type>
    <publisherName>Technische Universität, Institut für Akustik und Sprachkommunikation</publisherName>
    <publisherPlace>Dresden</publisherPlace>
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    <completedDate>2014-10-22</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="deu">Hidden Markov Model training using Finite State Machines</title>
    <identifier type="url">http://www.tu-cottbus.de/fakultaet3/de/kommunikationstechnik/publikationen/reports.html</identifier>
    <identifier type="url">https://www-docs.b-tu.de/fg-kommunikationstechnik/btuauth/_Publikationen/2010_hmmtrn.pdf</identifier>
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    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
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  <doc>
    <id>12240</id>
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    <publishedYear>2013</publishedYear>
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    <language>eng</language>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2014-10-22</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Voice control for measurement devices</title>
    <parentTitle language="eng">AIA-DAGA 2013, proceedings of the International Conference on Acoustics , 18 - 21 March 2013 in Merano</parentTitle>
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    <author>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Dieter</firstName>
      <lastName>Joneit</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Guntram</firstName>
      <lastName>Strecha</lastName>
    </author>
    <author>
      <firstName>Rüdiger</firstName>
      <lastName>Hoffmann</lastName>
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    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
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    <collection role="old_institute" number="03029">LS Kommunikationstechnik</collection>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
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  <doc>
    <id>18450</id>
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    <publishedYear>2017</publishedYear>
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    <language>deu</language>
    <pageFirst>S. 192</pageFirst>
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    <publisherName>DGZIP</publisherName>
    <publisherPlace>Berlin</publisherPlace>
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    <completedDate>2017-02-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Miniaturisiertes System zur intelligenten Signalverarbeitung</title>
    <parentTitle language="deu">DGZFP-Jahrestagung 2017, Zerstörungsfreie Materialprüfung, Koblenz, 22.-24.05.2017, Kurzfassungen der Vorträge und Posterbeiträge</parentTitle>
    <identifier type="url">https://www.google.de/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=5&amp;ved=0ahUKEwiOtpCM7-zUAhXBZ1AKHXMyBQYQFghDMAQ&amp;url=https%3A%2F%2Fjahrestagung.dgzfp.de%2FPortals%2F151%2Fdownloads%2FKufa_alles_oP%2520-%2520GESAMT%2520-%2520ohne%2520Anzeigen.pdf%3Fver%3D2017-05-19-135955-633&amp;usg=AFQjCNGFKZug7Xf1FFhVCqafxEFIyDEIeA&amp;cad=rja</identifier>
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    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Blüthgen</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Richter</lastName>
    </author>
    <author>
      <firstName>Gunther</firstName>
      <lastName>Papsdorf</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
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  <doc>
    <id>18451</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
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    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
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    <issue/>
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    <type>report</type>
    <publisherName>Fraunhofer-IKTS</publisherName>
    <publisherPlace>Dresden</publisherPlace>
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    <contributingCorporation>Fraunhofer-IKTS Dresden</contributingCorporation>
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    <completedDate>2017-02-02</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="deu">Akustische Mustererkennung: Qualitätskontrolle - Vorausschauende Instandhaltung - Zustandsüberwachung</title>
    <identifier type="url">https://www.ikts.fraunhofer.de/content/dam/ikts/forschung/energiesysteme_bio_medizintechnik/bio_nanotechnologie/akustische_diagnostik/IKTS_White_Paper_Akustische_Mustererkennung.pdf</identifier>
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    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
  </doc>
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    <completedYear/>
    <publishedYear>2021</publishedYear>
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    <language>deu</language>
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    <pageLast/>
    <pageNumber>9</pageNumber>
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    <completedDate>2024-01-29</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="deu">Sprachtechnologie und akustische Mustererkennung in der medizinischen Anwendung</title>
    <parentTitle language="deu">16. Landeskonferenz „Digitalisierung im Gesundheitswesen“ 2021</parentTitle>
    <identifier type="url">https://www.digital-agentur.de/veranstaltungen/telemed</identifier>
    <identifier type="url">https://www.digital-agentur.de/fileadmin/06_Bilddatenbank/Gesundheit/Telemed/Praesentationen/Tscho__pe_Impuls_Telemed21.pdf</identifier>
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      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
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    <author>
      <firstName>Frank</firstName>
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    </author>
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      <lastName>Wolff</lastName>
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    <id>36207</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast>8</pageLast>
    <pageNumber>8</pageNumber>
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    <issue/>
    <volume>37</volume>
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    <publisherName>AIP Publishing</publisherName>
    <publisherPlace>College Park, Maryland</publisherPlace>
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    <completedDate>2025-06-12</completedDate>
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    <title language="eng">Advanced methods in NDE using machine learning approaches</title>
    <abstract language="eng">Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability prediction based on big data becomes possible, even if components are used in different versions or configurations. This is the promise behind German Industry 4.0.</abstract>
    <parentTitle language="eng">44th Annual Review of Progress in Quantitative Nondestructive Evaluation, Provo, Utah, USA, 16-21 July 2017</parentTitle>
    <identifier type="doi">10.1063/1.5031519</identifier>
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    <title language="eng">The HMM synthesis algorithm of an embedded unified speech recognizer and synthesizer</title>
    <parentTitle language="eng">Proceedings of the Annual Conference of the International Speech Communication Association 2009, Interspeech 2009, 6 - 10 September, 2009, Brighton, UK</parentTitle>
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    <title language="eng">A study for laser additive manufacturing quality and material classification using machine learning</title>
    <abstract language="eng">This paper demonstrates the use of acoustic emissions (AEs) to monitor the quality, and material used, for the laser additive manufacturing (LAM) process with steel and copper wire. Layers of deposited material (steel or copper) were created using LAM. The quality of these layers was either good or unstable. The AEs were recorded using three sensors, one microphone, and two structure-borne sound probes. The recorded signals were processed and transformed using the fast Fourier method. Then models were trained with the processed data and evaluated using a fivefold cross-validation. Results show that it is possible to accurately classify the materials used during LAM (up to a balanced accuracy [BAcc] score of 0.99). Also, the process quality could be classified with a BAcc score of up to 0.81. Overall, the results are promising, but further research and data collection are necessary for a proper validation of our results.</abstract>
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      <firstName>Jorg</firstName>
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      <value>Machine learning</value>
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      <value>Neural network</value>
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      <value>Quality monitoring</value>
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      <value>Signal processing</value>
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      <value>Artificial intelligence</value>
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    <subject>
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    <completedDate>2025-06-12</completedDate>
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    <title language="deu">Materialdiagnose und integrale Prüfverfahren für keramische Bauteile</title>
    <abstract language="deu">Hochleistungskeramiken findet man heute häufig als kritische Komponente in neuentwickelten Systemen für Zukunftsanwendungen. Die Zuverlässigkeit des gesamten Systems basiert hierbei oft auf der kritischen keramischen Komponente. Für diese oft neuentwickelten keramischen Materialien werden neue Methoden für die Prozesssteuerung, Materialdiagnostik und Strukturüberwachung benötigt. In diesem Artikel werden drei für die Keramikcharakterisierung am Fraunhofer-Institut für Keramische Technologien und Systeme IKTS weiter entwickelte Technologien und Verfahren beschrieben und deren Einsatz anhand von Beispielen illustriert. Dazu werden die Laser-Speckle-Photometrie, die optische Kohärenztomographie und die Klanganalyse in Kombination mit einer entsprechenden akustischen Mustererkennung als leistungsfähige Verfahren für die Materialdiagnostik im Bereich der keramischen Materialien vorgestellt.</abstract>
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    <additionalTitle language="eng">Materials diagnostics and integrated testing technology for ceramic parts</additionalTitle>
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    <title language="eng">Speech-to-text in upper sorbian : current state</title>
    <abstract language="eng">This study presents recent advancements in Upper Sorbian&#13;
Speech-to-Text (STT) technology. We provide an overview of the Sorbian&#13;
languages, the available speech and language resources, and the development&#13;
of an STT system based on a traditional approach, which includes acoustic,&#13;
pronunciation, and language modeling.&#13;
Due to the scarcity of resources for Sorbian languages, our approach leverages&#13;
sub-word and word-class modeling techniques. The word-class modeling is based&#13;
on Finite-State Transducer definitions, which are applicable to both offline text&#13;
parsing and integration into the decoding graph of the STT system. Word-class&#13;
parsing is performed on the speech corpus and utilized for language modeling with&#13;
complete words, sub-word units, or both. Additionally, the same definitions can&#13;
be applied to Named Entity Recognition during the post-processing of recognized&#13;
transcriptions.&#13;
This approach significantly reduces out-of-vocabulary words and enables greater&#13;
customization of the recognizer for domain-specific applications. The system was&#13;
implemented for the real-time transcription of church sermon broadcasts in Upper&#13;
Sorbian. The domain-specific system achieved performance comparable to&#13;
fine-tuned OpenAI Whisper models developed also by other initiatives while also&#13;
providing a resource-efficient solution with semantically tagged recognition results.</abstract>
    <parentTitle language="eng">Elektronische Sprachsignalverarbeitung 2025 : Tagungsband der 36. Konferenz Halle/Saale, 5.–7. März 2025</parentTitle>
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      <lastName>Grawunder</lastName>
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      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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    <author>
      <firstName>Daniel</firstName>
      <lastName>Sobe</lastName>
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    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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    <author>
      <firstName>Matthias</firstName>
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    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
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    <title language="eng">Preserving Language Heritage Through Speech Technology: The Case of Upper Sorbian</title>
    <abstract language="eng">The modern world is facing a crisis with the rapid disappearance of endangered languages, which poses a serious threat to global cultural diversity. Speech Technologies and Artificial Intelligence present promising opportunities to address this crisis by supporting the documentation, revitalization, and everyday use of these vulnerable languages. However, despite recent and remarkable advancements in speech technology, significant challenges persist, particularly for languages with very limited resources and unique linguistic features.&#13;
&#13;
This paper details the development of Upper Sorbian speech technologies, focusing on the creation of a practical Speech-to-Text (STT) system as a versatile tool for language preservation. The study explores the current state of Sorbian languages and underscores collaborative efforts with the Foundation for the Sorbian People. Through a series of pilot and successive projects, each phase has contributed to the steady advancement of speech recognition modules and supporting tools, improving their performance, effectiveness and practical usability.</abstract>
    <parentTitle language="eng">Speech and Computer, SPECOM 2024, Belgrade, Serbia, 25-28 November 2024</parentTitle>
    <identifier type="isbn">978-3-031-77960-2</identifier>
    <identifier type="isbn">978-3-031-77961-9</identifier>
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    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <editor>
      <firstName>Alexey</firstName>
      <lastName>Karpov</lastName>
    </editor>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <editor>
      <firstName>Vlado</firstName>
      <lastName>Delic</lastName>
    </editor>
    <author>
      <firstName>Daniel</firstName>
      <lastName>Sobe</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Endangered languages, Speech recognition, Upper Sorbian</value>
    </subject>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
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    <id>36204</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>21</pageLast>
    <pageNumber>21</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>7</volume>
    <type>articler</type>
    <publisherName>MDPI AG</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Inline monitoring of battery electrode lamination processes based on acoustic measurements</title>
    <abstract language="eng">Due to the energy transition and the growth of electromobility, the demand for lithium-ion batteries has increased in recent years. Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. Signal analysis and machine learning techniques were used to acoustically distinguish the individual components that have been processed. This way, the detection of components with a balanced accuracy of up to 83% was possible, proving the feasibility of the concept as an inline capable monitoring system.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="doi">10.3390/batteries7010019</identifier>
    <identifier type="issn">2313-0105</identifier>
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Volume 3: Joint MSEC-NAMRC Symposia, 06272016."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s00170-016-9366-x","article-title":"Defects monitoring of laser metal deposition using acoustic emission sensor","volume":"90","author":"Gaja","year":"2016","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"020006","DOI":"10.1063\/1.5031503","article-title":"In-situ acoustic signature monitoring in additive manufacturing processes","volume":"1949","author":"Koester","year":"2018","journal-title":"AIP Conf. Proc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1109\/TII.2019.2910524","article-title":"Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission","volume":"15","author":"Shevchik","year":"2019","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_22","unstructured":"Chollet, F. (2018). Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek, MITP."},{"key":"ref_23","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_24","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., and Buhmann, J.M. (2010, January 23\u201326). The Balanced Accuracy and Its Posterior Distribution. 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      <firstName>Stefan</firstName>
      <lastName>Blume</lastName>
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      <firstName>Dario</firstName>
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      <firstName>Christoph</firstName>
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      <firstName>Klaus</firstName>
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    <title language="deu">Verfahren zur akustischen Detektion von Schadinsekten bei der Getreide- und Saatgutlagerung</title>
    <abstract language="deu">Getreide bildet eine der wichtigsten Grundlagen für die menschliche und tierische Ernährung. Wie jedes andere pflanzliche Produkt, so ist jedoch Getreide auch Nahrungsquelle für zahlreiche Insekten, die während des Pflanzenwachstums und der Lagerung das Korn befallen können. Jährlich werden etwa 10 % der weltweiten Getreideernte durch Schadinsekten vernichtet. In der Praxis wird der Befall mit Schadinsekten im Rahmen von Stichproben durch visuelle Inspektion, Sieben oder Aufschwemmen der Getreidekörner dokumentiert. Es werden auch einfache Handgeräte mit Mikrofon und Verstärker eingesetzt, um Schädlingsbefall durch Fressgeräusche manuell zu erkennen. Eine frühzeitige Diagnose des Auftretens von Vorratsschädlingen ist für die Einleitung von Gegenmaßnahmen und die Begrenzung des Schadens von großer Bedeutung. Das hier vorgestellte Verfahren bietet die Möglichkeit, die auftretenden Geräusche automatisiert zu analysieren, und ermöglicht so eine schnelle und zuverlässige Erkennung von Schadinsekten. Im Rahmen der Untersuchungen wurden die auftretenden Geräusche von Schadinsekten in den verschiedenen Entwicklungsstadien analysiert und eine geeignete Signalerfassungshardware für den Feldeinsatz entwickelt. Mit den implementierten Signalverarbeitungsalgorithmen der akustischen Mustererkennung erfolgt die automatisierte Auswertung und Klassifizierung der Schädlingsgeräusche. Das Verfahren, die Hardware und die Messergebnisse werden vorgestellt.</abstract>
    <parentTitle language="deu">DGZfP-Jahrestagung Zerstörungsfreie Materialprüfung 2017 : Kurzfassungen der Vorträge und Posterbeiträge : 22.-24. Mai 2017, Koblenz</parentTitle>
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    <author>
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    <author>
      <firstName>Paul</firstName>
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      <firstName>Matthias</firstName>
      <lastName>Pallmer</lastName>
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      <firstName>Olaf</firstName>
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      <firstName>Elisabeth</firstName>
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    <title language="deu">Sprachsteuerung für Mess- und Prüfgeräte</title>
    <parentTitle language="deu">DGZfP-Jahrestagung 2011 Zerstörungsfreie Materialprüfung, 30. Mai - 1. Juni 2011, Bremen, Berichtsband</parentTitle>
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    <author>
      <firstName>Constanze</firstName>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Dieter</firstName>
      <lastName>Joneit</lastName>
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      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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    <author>
      <firstName>Rüdiger</firstName>
      <lastName>Hoffmann</lastName>
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    <author>
      <firstName>Guntram</firstName>
      <lastName>Strecha</lastName>
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    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
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    <completedDate>2014-10-22</completedDate>
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    <title language="deu">Zustandsüberwachung von Magnetventilen anhand der Schaltgeräusche</title>
    <parentTitle language="deu">ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014</parentTitle>
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    <author>
      <firstName>Constanze</firstName>
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    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Matthias</firstName>
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    <title language="eng">Acoustic Resonance Recognition of Coins</title>
    <abstract language="eng">In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.</abstract>
    <parentTitle language="eng">2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia</parentTitle>
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      <firstName>Ivan</firstName>
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      <firstName>Yong Chul</firstName>
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      <firstName>Constanze</firstName>
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      <firstName>Christian</firstName>
      <lastName>Richter</lastName>
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      <firstName>Matthias</firstName>
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    <abstract language="eng">In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods.</abstract>
    <parentTitle language="eng">Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II</parentTitle>
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    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
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    <editor>
      <firstName>Ilias</firstName>
      <lastName>Maglogiannis</lastName>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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    <editor>
      <firstName>Lazaros S.</firstName>
      <lastName>Iliadis</lastName>
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    <author>
      <firstName>Yong Chul</firstName>
      <lastName>Ju</lastName>
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    <editor>
      <firstName>Elias</firstName>
      <lastName>Pimenidis</lastName>
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    <author>
      <firstName>Constanze</firstName>
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    <author>
      <firstName>Matthias</firstName>
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    <title language="eng">A Cognitive User Interface for a Multi-Modal Human-Machine Interaction</title>
    <parentTitle language="eng">Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings</parentTitle>
    <identifier type="isbn">978-3-319-99578-6</identifier>
    <identifier type="doi">10.1007/978-3-319-99579-3</identifier>
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    <author>
      <firstName>Constanze</firstName>
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    <editor>
      <firstName>Alexey</firstName>
      <lastName>Karpov</lastName>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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    <editor>
      <firstName>Oliver</firstName>
      <lastName>Jokisch</lastName>
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      <firstName>Markus</firstName>
      <lastName>Huber</lastName>
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      <firstName>Rodmonga</firstName>
      <lastName>Potapova</lastName>
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      <firstName>Werner</firstName>
      <lastName>Meyer</lastName>
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      <firstName>Matthias</firstName>
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    <publishedYear>2017</publishedYear>
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    <completedDate>2017-12-04</completedDate>
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    <title language="eng">Towards an Autarkic Embedded Cognitive User Interface</title>
    <abstract language="deu">ucuikt2015</abstract>
    <parentTitle language="eng">Proceedings Interspeech 2017, 20-24 August 2017, Stockholm</parentTitle>
    <identifier type="doi">10.21437/Interspeech.2017</identifier>
    <identifier type="url">http://www.isca-speech.org/archive/Interspeech_2017/</identifier>
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    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
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    <editor>
      <firstName>Francisco</firstName>
      <lastName>Lacerda</lastName>
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      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
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    <author>
      <firstName>Markus</firstName>
      <lastName>Huber</lastName>
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      <firstName>Werner</firstName>
      <lastName>Meyer</lastName>
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      <firstName>Oliver</firstName>
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      <firstName>Constanze</firstName>
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      <firstName>Matthias</firstName>
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    <title language="eng">Intelligent Signal Processing on a Miniaturized Hardware Module</title>
    <parentTitle language="eng">IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017</parentTitle>
    <identifier type="isbn">978-1-5090-1012-7</identifier>
    <identifier type="doi">10.1109/ICSENS.2017.8234023</identifier>
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    <note>IEEE Catalog Numer: CFP17SEN-ART</note>
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    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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      <firstName>Doreen</firstName>
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    <author>
      <firstName>Frank</firstName>
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    <author>
      <firstName>Christian</firstName>
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    <author>
      <firstName>Peter</firstName>
      <lastName>Blüthgen</lastName>
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      <firstName>Matthias</firstName>
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    <title language="eng">An embedded system for acoustic pattern recognition</title>
    <parentTitle language="eng">IEEE SENSORS Proceedings, Glasgow, Scotland, UK,  Oct. 29 - Nov. 1, 2017</parentTitle>
    <identifier type="isbn">978-1-5090-1012-7</identifier>
    <identifier type="doi">10.1109/ICSENS.2017.8234380</identifier>
    <note>IEEE Catalog Numer: CFP17SEN-ART</note>
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    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
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    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Christian</firstName>
      <lastName>Richter</lastName>
    </author>
    <author>
      <firstName>Peter</firstName>
      <lastName>Blüthgen</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
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  <doc>
    <id>28458</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>43</pageFirst>
    <pageLast>50</pageLast>
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    <edition/>
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    <publisherName>TUDpress</publisherName>
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    <completedDate>2022-01-21</completedDate>
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    <title language="deu">Cross-Lingual Acoustic Modeling in Upper Sorbian – Preliminary Study</title>
    <abstract language="deu">In this paper, we present a preliminary study for acoustic modeling in Upper Sorbian, where a model of German was used in cross-lingual transfer learning. At first, we define the grapheme and phoneme inventories and map the target phonemes from the most similar German source equivalents. Phonetically balanced sentences for the recording prompts were selected from a combination of general and domain-specific textual data. The speech corpora with a total duration of around 11 hours was collected in controlled recording sessions involving an equal number of females, males, and children. The baseline acoustic model was employed to force-align the speech corpora given the knowledge-based phoneme mappings. How well the mappings were, was evaluated by the phoneme confusions in free-phoneme recognition. The new derived data-driven model with a reduced phoneme set was included in the adaptation and evaluation along with the baseline acoustic model. The model adaptation performance was cross-validated with the “Leave One Group Out” strategy. We observed major improvements in phoneme error rates after adaptation for the knowledge-based and data-driven phoneme mappings. The study confirmed the feasibility of transfer learning for acoustic model adaptation in the case of Upper Sorbian, at the same time demonstrating practical usability with a small vocabulary speech recognition application (Smart Lamp).</abstract>
    <parentTitle language="deu">Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. März 2021</parentTitle>
    <identifier type="isbn">978-3-959082-27-3</identifier>
    <identifier type="issn">0940-6832</identifier>
    <identifier type="url">https://publica.fraunhofer.de/dokumente/N-633297.html</identifier>
    <enrichment key="UBICOseries">Studientexte zur Sprachkommunikation ; Band 99</enrichment>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <editor>
      <firstName>Stefan</firstName>
      <lastName>Hillmann</lastName>
    </editor>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Marek</firstName>
      <lastName>Rjelka</lastName>
    </author>
    <editor>
      <firstName>Benjamin</firstName>
      <lastName>Weiss</lastName>
    </editor>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <editor>
      <firstName>Thilo</firstName>
      <lastName>Michael</lastName>
    </editor>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <editor>
      <firstName>Sebastian</firstName>
      <lastName>Möller</lastName>
    </editor>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
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  </doc>
  <doc>
    <id>27074</id>
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    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>11000</pageFirst>
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    <issue>9</issue>
    <volume>21</volume>
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    <completedDate>2021-03-02</completedDate>
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    <title language="eng">Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace</title>
    <abstract language="eng">We investigated and compared various algorithms in machine learning for anomaly assessment with different feature analyses on ultrasonic signals recorded by sensor networks. The following methods were used and compared in anomaly detection modeling: hidden Markov models (HMM), support vector machines (SVM), isolation forest (IF), and reconstruction autoencoders (AEC). They were trained exclusively on sensor signals of the intact state of structures commonly used in various industries, like aerospace and automotive. The signals obtained on artificially introduced damage states were used for performance evaluation. Anomaly assessment was evaluated and compared using various classifiers and feature analysis methods. We introduced novel methodologies for two processes. The first was the dataset preparation with anomalies. The second was the detection and damage severity assessment utilizing the intact object state exclusively. The experiments proved that robust anomaly detection is practically feasible. We were able to train accurate classifiers which had a considerable safety margin. Precise quantitative analysis of damage severity will also be possible when calibration data become available during exploitation or by using expert knowledge.</abstract>
    <parentTitle language="eng">IEEE Sensors Journal</parentTitle>
    <identifier type="issn">1558-1748</identifier>
    <identifier type="url">https://ieeexplore.ieee.org/document/9366491</identifier>
    <identifier type="doi">10.1109/JSEN.2021.3062941</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Non-destructive testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Ultrasonic transducers</value>
    </subject>
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    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
  </doc>
  <doc>
    <id>27760</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1001</pageFirst>
    <pageLast>1005</pageLast>
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    <edition/>
    <issue/>
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    <contributingCorporation>ISCA</contributingCorporation>
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    <completedDate>2021-09-14</completedDate>
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    <title language="eng">Glottal Stops in Upper Sorbian: A Data-Driven Approach</title>
    <abstract language="eng">We present a data-driven approach for the quantitative analysis of glottal stops before word-initial vowels in Upper Sorbian, a West Slavic minority language spoken in Germany. Glottal stops are word-boundary markers and their detection can improve the performance of automatic speech recognition and speech synthesis systems.&#13;
&#13;
We employed cross-language transfer using an acoustic model in German to develop a forced-alignment method for the phonetic segmentation of a read-speech corpus in Upper Sorbian. The missing phonemic units were created by combining the existing phoneme models. In the forced-alignment procedure, the glottal stops were considered optional in front of word-initial vowels.&#13;
&#13;
To investigate the influence of speaker type (males, females, and children) and vowel on the occurrence of glottal stops, binomial regression analysis with a generalized linear mixed model was performed. Results show that children glottalize word-initial vowels more frequently than adults, and that glottal stop occurrences are influenced by vowel quality.</abstract>
    <parentTitle language="eng">Proc. Interspeech 2021, 30 August – 3 September, 2021, Brno, Czechia</parentTitle>
    <identifier type="url">https://www.isca-speech.org/archive/interspeech_2021/kraljevski21_interspeech.html</identifier>
    <identifier type="doi">10.21437/Interspeech.2021-1101</identifier>
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    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Maria Paola</firstName>
      <lastName>Bissiri</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
  </doc>
  <doc>
    <id>26807</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>4</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <publisherName/>
    <publisherPlace>Rotterdam, Netherlands</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation>IEEE</contributingCorporation>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-01-21</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors</title>
    <abstract language="eng">We present a method for extracting health indicators from piezoelectric sensors applied in the case of microfluidic valves. Convolutional autoencoders were used to train a model on the normal operating conditions and tested on signals of different valves. The results of the model performance evaluation, as well as, the qualitative presentation of the indicator plots for each tested component, showed that the used approach is capable of detecting features that correspond to increasing component degradation. The extracted health indicators are the prerequisite and input for reliable remaining useful life prediction.</abstract>
    <parentTitle language="eng">2020 IEEE Sensors, 25-28 Oct. 2020, Rotterdam, Netherlands,</parentTitle>
    <identifier type="url">https://ieeexplore.ieee.org/document/9323023</identifier>
    <identifier type="doi">10.1109/SENSORS47125.2020.9323023</identifier>
    <identifier type="isbn">978-1-7281-6801-2</identifier>
    <enrichment key="BTU">an der BTU erstellt / created at BTU</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
  </doc>
  <doc>
    <id>30199</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>28</pageFirst>
    <pageLast>35</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
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    <publisherName>European Language Resources Association</publisherName>
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    <completedDate>2023-01-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Word Class Based Language Modeling: A Case of Upper Sorbian</title>
    <abstract language="eng">In this paper we show how word class based language modeling can support the integration of a small language in modern applications of speech technology. The methods described in this paper can be applied for any language. We demonstrate the methods on Upper Sorbian. The word classes model the semantic expressions of numerals, date and time of day. The implementation of the created grammars was realized in the form of finite-state-transducers (FSTs) and minimalists grammars (MGs). We practically demonstrate the usage of the FSTs in a simple smart-home speech application, that is able to set wake-up alarms and appointments expressed in a variety of spontaneous and natural sentences. While the created MGs are not integrated in an application for practical use yet, they provide evidence that MGs could potentially work more efficient than FSTs in built-on applications. In particular, MGs can work with a significantly smaller lexicon size, since their more complex structure lets them generate more expressions with less items, while still avoiding wrong expressions.</abstract>
    <parentTitle language="eng">Proceedings of The Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference, Marseille, France</parentTitle>
    <identifier type="isbn">978-2-493814-07-4</identifier>
    <identifier type="url">http://www.lrec-conf.org/proceedings/lrec2022/workshops/EURALI/pdf/2022.eurali-1.5.pdf</identifier>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Isidor Konrad</firstName>
      <lastName>Maier</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Johannes Ferdinand Joachim</firstName>
      <lastName>Kuhn</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
    </author>
    <author>
      <firstName>Daniel</firstName>
      <lastName>Sobe</lastName>
    </author>
    <author>
      <firstName>Matthias</firstName>
      <lastName>Wolff</lastName>
    </author>
    <author>
      <firstName>Constanze</firstName>
      <lastName>Tschöpe</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>word classes, minimalist grammar, language modeling, speech recognition, Upper Sorbian</value>
    </subject>
    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
  </doc>
  <doc>
    <id>30156</id>
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    <completedDate>2023-01-24</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Word Class Based Language Modeling: A Case of Upper Sorbian, LREC 2022, Marseille</title>
    <parentTitle language="deu">Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13</parentTitle>
    <identifier type="doi">10.5281/zenodo.7501145</identifier>
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    <enrichment key="Fprofil">4 Künstliche Intelligenz und Sensorik / Artificial Intelligence and Sensor Technology</enrichment>
    <author>
      <firstName>Isidor Konrad</firstName>
      <lastName>Maier</lastName>
    </author>
    <submitter>
      <firstName>Doreen</firstName>
      <lastName>Hobracht</lastName>
    </submitter>
    <author>
      <firstName>Johannes</firstName>
      <lastName>Kuhn</lastName>
    </author>
    <author>
      <firstName>Frank</firstName>
      <lastName>Duckhorn</lastName>
    </author>
    <author>
      <firstName>Ivan</firstName>
      <lastName>Kraljevski</lastName>
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    <author>
      <firstName>Daniel</firstName>
      <lastName>Sobe</lastName>
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    <author>
      <firstName>Matthias</firstName>
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    <author>
      <firstName>Constanze</firstName>
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    <collection role="institutes" number="1104">FG Kommunikationstechnik</collection>
    <collection role="institutes" number="1124">FG Kognitive Materialanalytik</collection>
  </doc>
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