Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods

TL;DR

This study evaluates feature selection methods for speech-based emotion recognition to reduce computational resources, demonstrating that smaller feature subsets can maintain or improve accuracy, facilitating resource-efficient health monitoring systems.

Contribution

It compares three state-of-the-art feature selection methods with a new approach, Active Feature Selection, for emotion recognition in resource-constrained environments.

Findings

Smaller feature subsets can achieve similar or better accuracy.

Resource-efficient models are feasible for health monitoring applications.

Active Feature Selection outperforms or matches existing methods.

Abstract

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.