Semi-supervised context adaptation: Case study of audience excitement recognition

Elena Vildjiounaite (Corresponding Author), Vesa Kyllönen, Satu-Marja Mäkelä, Olli Vuorinen, Tommi Keränen, Johannes Peltola, G. Gimel'farb

    Research output: Contribution to journalArticleScientificpeer-review

    3 Citations (Scopus)

    Abstract

    To recognise just the same human reaction (for example, a strong excitement) in different contexts, customary behaviours in these contexts have to be taken into account; e.g. a happy sport audience may be cheering for long time, while a happy theatrical audience may produce only short bursts of laughter in order to not interrupt the performance. Tailoring recognition algorithms to contexts can be achieved by building either a context-specific or a generic system. The former is individually trained for each context to recognise sets of characteristic responses, whereas the latter—in contrast to the context-specific one—adapts to the context via significantly more lightweight modification of parameters. This paper follows the latter way and proposes a simple modification of a hidden Markov model (HMM) classifier that enables end users to adapt the generic system to a context or a personal perception of an annotator by labelling a fairly small number of data samples of each context. For better adaptability to the limited number of the user’s annotations, the proposed semi-supervised HMM classifier employs the maximum posterior marginal, rather than the more conventional maximum a posteriori decision rule. The proposed user- and context-adaptable semi-supervised HMM classifier was tested on recognising excitement of a show audience in three contexts (a concert hall, a circus, and a sport event), differing in how the excitement is expressed. In our experiments the proposed classifier recognised reactions of a non-neutral audience with 10% higher accuracy than the conventional HMM and support vector machine based classifiers.
    Original languageEnglish
    Pages (from-to)231-250
    Number of pages20
    JournalMultimedia Systems
    Volume18
    Issue number3
    DOIs
    Publication statusPublished - 2012
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Audience responses
    • context adaptation
    • hidden Markov models
    • semi-supervised learning

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