Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Recognizing Emotions: Developing a Linguistic Understanding of How Emotions Feed Into Speech Through Semantics and Facial Expressions

Description

The importance of nonverbal communication has been well established through several theories including Albert Mehrabian's 7-38-55 rule that proposes the respective importance of semantics, tonality and facial expressions in communication. Although several studies have examined how emotions are expressed and preceived in communication, there is limited research investigating the relationshi…

The importance of nonverbal communication has been well established through several theories including Albert Mehrabian's 7-38-55 rule that proposes the respective importance of semantics, tonality and facial expressions in communication. Although several studies have examined how emotions are expressed and preceived in communication, there is limited research investigating the relationship between how emotions are expressed through semantics and facial expressions. Using a facial expression analysis software to deconstruct facial expressions into features and a K-Nearest-Neighbor (KNN) machine learning classifier, we explored if facial expressions can be clustered based on semantics. Our findings indicate that facial expressions can be clustered based on semantics and that there is an inherent congruence between facial expressions and semantics. These results are novel and significant in the context of nonverbal communication and are applicable to several areas of research including the vast field of emotion AI and machine emotional communication.
ContributorsEverett, Lauren (Author) / Coza, Aurel (Thesis director) / Santello, Marco (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2022-05