Speaker siglet detection for Business Microscope

Jun Nishimura, Nobuo Sato, Tadahiro Kuroda

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    "Business Microscope "is our sensornet application in the age of knowledge, which visualizes knowledge workers' interactions by sensing their face-to-face communications. Due to the limitation of energy consumption of sensor nodes and privacy concerns, very short (0.1s) intermittently sensed (10s interval) noise-like signals called siglet is used to for detection task. To detect the speaker from the limited input, "self vs "others" classification problem is introduced. For this new classification problem, new classifier called AdaBoost LVQ is studied to explore the application of AdaBoost to reduce the error rate of the conventional classifier with strictly limited inputs. As a result, AdaBoost LVQ achieved highest recognition accuracy of 96.45% with 19.86% error rate improvement relative to best conventional classifier.

    Original languageEnglish
    Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
    Pages376-381
    Number of pages6
    DOIs
    Publication statusPublished - 2008 Dec 1
    Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
    Duration: 2008 Dec 112008 Dec 13

    Publication series

    NameProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008

    Other

    Other7th International Conference on Machine Learning and Applications, ICMLA 2008
    CountryUnited States
    CitySan Diego, CA
    Period08/12/1108/12/13

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Science Applications
    • Software

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