  Research in automatic affect recognition has seldom addressed the issue of
computational resource utilization. With the advent of ambient intelligence
technology which employs a variety of low-power, resource-constrained devices,
this issue is increasingly gaining interest. This is especially the case in the
context of health and elderly care technologies, where interventions may rely
on monitoring of emotional status to provide support or alert carers as
appropriate. This paper focuses on emotion recognition from speech data, in
settings where it is desirable to minimize memory and computational
requirements. Reducing the number of features for inductive inference is a
route towards this goal. In this study, we evaluate three different
state-of-the-art feature selection methods: Infinite Latent Feature Selection
(ILFS), ReliefF and Fisher (generalized Fisher score), and compare them to our
recently proposed feature selection method named `Active Feature Selection'
(AFS). The evaluation is performed on three emotion recognition data sets
(EmoDB, SAVEE and EMOVO) using two standard acoustic paralinguistic feature
sets (i.e. eGeMAPs and emobase). The results show that similar or better
accuracy can be achieved using subsets of features substantially smaller than
the entire feature set. A machine learning model trained on a smaller feature
set will reduce the memory and computational resources of an emotion
recognition system which can result in lowering the barriers for use of health
monitoring technology.
