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.
@InProceedings{FAYET18.714, author = {Cédric Fayet and Arnaud Delhay and Damien Lolive and Pierre-françois Marteau}, title = "{EMO&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
Summary of the paper