Voice analytics in business research: Conceptual foundations, acoustic feature extraction, and applications

Recent advances in artificial intelligence and natural language processing are gradually transforming how humans search, shop, and express their preferences. Leveraging the new affordances and modalities of human–machine interaction through voice-controlled interfaces will require a nuanced understa...

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Bibliographic Details
Published in:Journal of business research 2020-12, Vol.121, p.364-374
Main Authors: Hildebrand, Christian, Efthymiou, Fotis, Busquet, Francesc, Hampton, William H., Hoffman, Donna L., Novak, Thomas P.
Format: Article
Language:English
Subjects:
Acoustic markers of emotion
Artificial intelligence
Business
Business schools
College teachers
Computational linguistics
Database industry
Emotion detection
Language processing
Natural language interfaces
Natural language processing
Visualization (Computers)
Voice Analytics
Voice-controlled interfaces
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Summary:Recent advances in artificial intelligence and natural language processing are gradually transforming how humans search, shop, and express their preferences. Leveraging the new affordances and modalities of human–machine interaction through voice-controlled interfaces will require a nuanced understanding of the physics and psychology of speech formation as well as the systematic extraction and analysis of vocal features from the human voice. In this paper, we first develop a conceptual framework linking vocal features in the human voice to experiential outcomes and emotional states. We then illustrate the effective processing, editing, analysis, and visualization of voice data based on an Amazon Alexa user interaction, utilizing state-of-the-art signal-processing packages in R. Finally, we offer novel insight into the ways in which business research might employ voice and sound analytics moving forward, including a discussion of the ethical implications of building multi-modal databases for business and society.
ISSN:0148-2963
1873-7978
DOI:10.1016/j.jbusres.2020.09.020