Fusion for Audio-Visual Laughter Detection

B. Reuderink

    Research output: Book/ReportReportProfessional

    38 Downloads (Pure)


    Laughter is a highly variable signal, and can express a spectrum of emotions. This makes the automatic detection of laughter a challenging but interesting task. We perform automatic laughter detection using audio-visual data from the AMI Meeting Corpus. Audio-visual laughter detection is performed by combining (fusing) the results of a separate audio and video classifier on the decision level. The video-classifier uses features based on the principal components of 20 tracked facial points, for audio we use the commonly used PLP and RASTA-PLP features. Our results indicate that RASTA-PLP features outperform PLP features for laughter detection in audio. We compared hidden Markov models (HMMs), Gaussian mixture models (GMMs) and support vector machines (SVM) based classifiers, and found that RASTA-PLP combined with a GMM resulted in the best performance for the audio modality. The video features classified using a SVM resulted in the best single-modality performance. Fusion on the decision-level resulted in laughter detection with a significantly better performance than single-modality classification.
    Original languageUndefined
    Place of PublicationEnschede
    PublisherCentre for Telematics and Information Technology (CTIT)
    Number of pages41
    Publication statusPublished - 3 Dec 2007

    Publication series

    NameCTIT Technical Report Series
    PublisherCentre for Telematics and Information Technology, University of Twente
    ISSN (Print)1381-3625


    • METIS-247035
    • IR-64474
    • HMI-CI: Computational Intelligence
    • EWI-11435

    Cite this