title: Strength Is in Numbers: Can Concordant Artificial Listeners Improve Prediction of Emotion from Speech?
abstract: Humans can communicate their emotions by modulating facial expressions or the tone of their voice. Albeit numerous applications exist that enable machines to read facial emotions and recognize the content of verbal messages, methods for speech emotion recognition are still in their infancy. Yet, fast and reliable applications for emotion recognition are the obvious advancement of present 'intelligent personal assistants', and may have countless applications in diagnostics, rehabilitation and research. Taking inspiration from the dynamics of human group decision-making, we devised a novel speech emotion recognition system that applies, for the first time, a semi-supervised prediction model based on consensus. Three tests were carried out to compare this algorithm with traditional approaches. Labeling performances relative to a public database of spontaneous speeches are reported. The novel system appears to be fast, robust and less computationally demanding than traditional methods, allowing for easier implementation in portable voice-analyzers (as used in rehabilitation, research, industry, etc.) and for applications in the research domain (such as real-time pairing of stimuli to participants' emotional state, selective/differential data collection based on emotional content, etc.).
authors: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
authors: Eugenio
authors: E
authors: Martinelli
authors: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
authors: Arianna
authors: A
authors: Mencattini
authors: Department of System Medicine and CBMS, University of Rome Tor Vergata, Rome, Italy.
authors: Elena
authors: http://orcid.org/0000-0002-0919-0475
authors: E
authors: Daprati
authors: Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
authors: Corrado
authors: C
authors: Di Natale
mesh_terms: D000162:Acoustics; D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D001307:Auditory Perception; D003198:Computer Simulation; D003299:Cooperative Behavior; D003657:Decision Making; D004644:Emotions; D005149:Facial Expression; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D021641:Recognition, Psychology; D013060:Speech; D013067:Speech Perception; D014831:Voice; D055815:Young Adult