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A Reporting Assistant for Railway Security Staff

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Published:22 July 2020Publication History

ABSTRACT

In this paper, we introduce RARSS, a reporting assistant for railway security staff. RARSS is a demonstration application with a multi-modal interface based on the Mobile Multimodal Interaction and Rendering (MMIR) framework. The system should support the security staff at railway premises (stations, trains, etc.) in Germany and inform about security relevant information about the travel of football fans or a group of people on their way to a major event. In the application we leverage multi keyword spotting (KWS) for detecting of the actual context and a grammar with specific voice commands to improve the semantic interpretation. The results of friendly user testing showed that the multimodal conversational interface was positively rated according the simplicity and the efficiency to make security reports by the security staff.

References

  1. Aaron Ruß. 2013. MMIR framework: multimodal mobile interaction and rendering. In INFORMATIK 2013 -- Informatik angepasst an Mensch, Organisation und Umwelt, Matthias Horbach (Ed.). Gesellschaft für Informatik e.V., Bonn, 2702--2713.Google ScholarGoogle Scholar

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  1. A Reporting Assistant for Railway Security Staff

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    • Published in

      cover image ACM Other conferences
      CUI '20: Proceedings of the 2nd Conference on Conversational User Interfaces
      July 2020
      271 pages
      ISBN:9781450375443
      DOI:10.1145/3405755

      Copyright © 2020 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 July 2020

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      Qualifiers

      • demonstration
      • Research
      • Refereed limited

      Acceptance Rates

      CUI '20 Paper Acceptance Rate13of39submissions,33%Overall Acceptance Rate34of100submissions,34%

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