Now showing 1 - 10 of 10
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Engineering Power Attributions in Conversational Agents: The Unexplored Impact of Vocal Vibrato and Vocal Tract Length

2021-07 , Efthymiou, Fotios

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Designing Vulnerable Conversational Agents: The Impact of Trembling Vocal Cues on Empathic Concern and Prosocial Behavior

2022-05 , Efthymiou, Fotios , Hildebrand, Christian Alexander

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How Big is That Voice? Vocal Features of Conversational AI Affects Physicality Perceptions and Product Congruency

2021 , Efthymiou, Fotios , Hampton, William Heyward , Hildebrand, Christian Alexander

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Morphing Vulnerable Machines: Paralinguistic Cues in Digital Voice Assistants Shape Perceptions of Physicality, Vulnerability and Trust

2020 , Efthymiou, Fotios , Hildebrand, Christian

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Conversational Commerce in Finance

2021 , Schär, Patrik , Hildebrand, Christian Alexander , Efthymiou, Fotios

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Designing Vulnerable Conversational AI: The Impact of Trembling Vocal Cues on Empathic Concern and Prosocial Behavior

2022 , Efthymiou, Fotios , Hildebrand, Christian Alexander

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How Big is That Voice? Vocal Tract Length of Conversational AI Affects Physicality Perceptions and Product Congruency

2021 , Efthymiou, Fotios , Hampton, William Heyward , Hildebrand, Christian Alexander

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Voice Analytics in Business Research: Conceptual Foundations, Acoustic Feature Extraction, and Applications.

2020 , Hildebrand, Christian Alexander , Efthymiou, Fotios , Busquet I Segui, Francesc , Hampton, William Heyward , Hoffmann, D.L. , Novak, P.T.

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Morphing Vulnerable Machines: Paralinguistic Cues in Digital Voice Assistants Shape Perceptions of Physicality, Vulnerability, And Trust

2021 , Efthymiou, Fotios

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Publication

Voice Analytics in Business Research: Conceptual Foundations, Acoustic Feature Extraction, and Applications

2020-10-16 , Busquet I Segui, Francesc , Efthymiou, Fotios , Hildebrand, Christian Alexander , Hampton, William Heyward , Hoffmann, Donna , Novak, Thomas

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. The current research offers novel insight into the ways in which future marketing scholars might employ voice and sound analytics moving forward, including a discussion of the ethical implications of building multi-modal databases for business and society. Finally, we present the MAFiA R package, a novel R package developed at our lab to automate the voice analytics process, especially suitable for social scientists and experimental researchers.