EMO&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context

Cédric Fayet, Arnaud Delhay, Damien Lolive, Pierre-François Marteau

Abstract: This paper presents a new corpus, called EMOLY (EMOtion and AnomaLY), composed of speech and facial video records of subjects that contains controlled anomalies. As far as we know, to study the problem of anomaly detection in discourse by using machine learning classification techniques, no such corpus exists or is available to the community. In EMOLY, each subject is recorded three times in a recording studio, by filming his/her face and recording his/her voice with a HiFi microphone. Anomalies in discourse are induced or acted. At this time, about 8,65 hours of usable audiovisual recording on which we have tested classical classification techniques (GMM or One Class-SVM plus threshold classifier) are available. Results confirm the usability of the anomaly induction mechanism to produce anomalies in discourse and also the usability of the corpus to improve detection techniques.