Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods. (January 2021)

• Record Type: Journal Article
• Authors: Haider, Fasih; Pollak, Senja; Albert, Pierre; Luz, Saturnino
• Is Part Of: Computer speech & language. Volume 65(2021)
• Publication Date: 2021-01
• Subjects: Feature engineering -- Feature selection -- Emotion recognition -- Affective computing -- Prosodic analysis -- Cognitive health monitoring,; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2020.101119
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Automatic feature selection methods improve emotion recognition performance. Systematic evaluation of four feature selection methods is performed on three datasets in three languages and two acoustic feature sets. Our recently introduced Active feature selection method is successfully tested on the emotion recognition task. With only 1/3 of eGeMAPs and 1/10 emobase features comparable results to the full feature set are obtained. 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.