This paper addresses the problem of speech emotion recognition
from movie audio tracks. The recently collected Acted Facial Ex-
pression in the Wild 5.0 database is used. The aim is to discrimi-
nate among angry, happy, and neutral. We extract a relatively small
number of features, a subset of which is not commonly used for the
emotion recognition task. Those features are fed as input to an en-
semble classifier that combines random forests with support vector
machines. An accuracy of 65.63% is reported, outperforming a base-
line system that uses the K-nearest neighbor classifier and has an ac-
curacy of 56.88%. To verify the suitability of the exploited features,
the same ensemble classification schema is applied on the feature set
similar those employed in Audio/Visual Emotion Challenge 2011. In
the latter case, an accuracy of 61.25% is achieved using a large set
of 1582 features, as opposed to just 86 features in our case that lead
to a relative improvement of 7.15% in accuracy.