Researchers at the University of Texas at Arlington have recently explored the use of machine learning for emotion recognition based solely on paralinguistic information. Paralinguistics are aspects of spoken communication that do not involve words, such as pitch, volume, intonation, etc.

Recent advances in machine learning have led to the development of tools that can recognize emotional states by analyzing images, voice recordings, electroencephalograms or electrocardiograms. These tools could have several interesting applications, for instance, enabling more efficient human-computer interactions in which a computer recognizes and responds to a human user’s emotions.

“In general, one may argue that speech carries two distinct types of information: explicit or linguistic information, which concerns articulated patterns by the speaker; and implicit or paralinguistic information, which concerns the variation in pronunciation of the linguistic patterns,” the researchers wrote in their paper, published in the Advances in Experimental Medicine and Biology book series. “Using either or both types of information, one may attempt to classify an audio segment that consists of speech, based on the emotion(s) it carries. However, emotion recognition from speech appears to be a significantly difficult task even for a human, no matter if he/she is an expert in this field (e.g. a psychologist).”