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.).
Highlights
One of the most irritating features of virtual receptionists is their being utterly impermeable to the emotional outbursts of callers, who, feel more neglected and less satisfied than when interacting with human attendants
We describe a novel speech emotion recognition system that applies a semi-supervised prediction model based on consensus
This approach deeply departs from procedures like active learning [10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40] and from self-training [20]
Summary
One of the most irritating features of virtual receptionists is their being utterly impermeable to the emotional outbursts of callers, who, feel more neglected and less satisfied than when interacting with human attendants. Despite complexity of the non-verbal signals conveyed by the voice, humans recognize them, and react . Machines do not detect the emotional information embedded in the voice and, the human partner may become annoyed by the apparent lack of empathy. It is not surprising that speech emotion recognition systems (SER) have recently become of interest to the domain of human-machine interfaces [1,2], their application is relevant for treatment of psychiatric and neurologic conditions affecting the emotional sphere (e.g. autism [3,4] Parkinson Disease [5,6,7], mood disorders [8]).
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