Corpus annotation is an important aspect in speech applications where stochastic models need to be trained and evaluated. Multimodal corpora are also annotated. Moreover, corpus annotation is an essential phase in the construction of emotion recognizer engines. Large corpora, as they are essential to construct representative knowledge bases, have been a problem for corpus annotators. Time consumed for labeling such corpora is very significant. Furthermore, manageability becomes more arduous and tedious. In this paper, we propose a semi-automatic tool, called BECAM tool, that will help corpus annotators in managing and annotating large sample emotion corpora.
Bibliographic reference. Abdennadher, Slim / Aly, Mohamed / Bühler, Dirk / Minker, Wolfgang / Pittermann, Johannes (2007): "BECAM tool - a semi-automatic tool for bootstrapping emotion corpus annotation and management", In INTERSPEECH-2007, 946-949.