An ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech

In this paper, we present a detection approach and an ‘open-set’ detection evaluation methodology for automatic emotion recognition in speech. The traditional classification approach does not seem to be suitable and flexible enough for typical emotion recognition tasks. For example, classification does not have an appropriate way to cope with ‘new’ emotions from outside the training database that can be encountered in real life situations. With the presented approach and evaluation method we aim to perform experiments that suit the task of an emotion detector and to obtain results that are better interpretable and generalizable to other datasets. This evaluation method takes into account the fact that ‘new’ emotions can be encountered by the emotion detectors that are not covered by the trained models. We also tested the trained emotion detectors on an independent emotion database. We report results that indicate that our ‘openset’ evaluation methodology indeed produces more representative and generizable results.