Towards a model of arousal change after affective word pronunciation based on electrodermal activity and speech analysis. (May 2021)

• Record Type: Journal Article
• Authors: Marzi, Claudia; Greco, Alberto; Scilingo, Enzo Pasquale; Vanello, Nicola
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Speech -- Electrodermal activity -- Statistical models -- Arousal -- Word pronunciation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102517
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Modeling subject arousal during reading aloud task. Electrodermal activity and speech features can jointly model arousal perception. A novel possible method for validating self-assessment emotional ratings. Complementary information from speech and electrodermal features for arousal modeling. Informative content of electrodermal activity is not hidden by concurrent speaking. Abstract: In this paper, we explore the possibility of building a model of subject arousal by exploiting the acquisition and the analysis of speech and electrodermal activity (EDA). Several issues have to be addressed to reach this goal as the estimation of the relationship between arousal and behavioral measures and the reliability of EDA signal during speech production. To accomplish this task, we will investigate the relation among EDA, speech activity and subject arousal, during isolated affective word pronunciation. Our results show that significant information on subject arousal can be obtained by analyzing EDA during the processing of out-of-context words with an emotional content in a reading aloud task. Based on a sample of eighteen Italian participants, we observed a significant relation between EDA features and self-reported arousal scores. Quantitative models relating EDA- and speech-derived features are proposed and discussed. We found that increasing values of tonic and phasic components of EDA signals correspond to increasing self-assessed arousal scores; Mel-frequency cepstral analysis of speech was also shown to carry relevant information about subject arousal, with a significant inverse relation to self-assessed scores. Our results suggest how the analysis of concurrent acquisition of EDA and speech features may offer a valid approach for the prediction of subject arousal during speech production, as well as a method for validating self-assessment ratings themselves.