Matching Items (2)
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The Language of Revim: A Constructed Language

Description
This is a constructed language that is primarily based on getting rid of Morphological and Syntactical ambiguity. Much of the inspiration I took when making this language came from Latin and Ancient Greek, adopting the things that make those languages beautiful, and changing the things that make them difficult. The…

This is a constructed language that is primarily based on getting rid of Morphological and Syntactical ambiguity. Much of the inspiration I took when making this language came from Latin and Ancient Greek, adopting the things that make those languages beautiful, and changing the things that make them difficult. The main thing I wanted this language to accomplish was to achieve maximum specificity, clarity, and complexity of thought, and therefore I focused heavily on clause formation and making this a highly synthetic language.
ContributorsJennings, Elisa Tamara (Author) / Van Gelderen, Elly (Thesis director) / Pruitt, Kathryn (Committee member) / School of International Letters and Cultures (Contributor) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
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Deep Learning with Virtual Agents: How Accented and Synthetic Voices Affect Outcomes

Description
The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent…

The current study investigates accent effects using virtual agents in the context of a multimedia learning environment. In a 2 (voice type: human, synthetic) x 2 (voice accent: English, Russian) between-subjects factorial design, the source and accent of the agent’s voice were manipulated. Research has shown that an instructor’s accent can have an impact on learning outcomes and perceptions of the instructor. However, these outcomes and perceptions have yet to be fully understood in the context of a virtual human instructor. Outcome measures collected included: knowledge retention, knowledge transfer, and cognitive load. Perception measures were collected using the Agent Persona Instrument-Revised, API-R, and a speaker-rating survey. Overall, there were no significant differences between the accented conditions. However, the synthetic condition had significantly lower knowledge retention, knowledge transfer, and mental effort efficiency than the professional voices in the human condition. Participants rated the human recordings higher on speaker-rating and API-R measures. These findings demonstrate the importance of considering the quality of the voice when designing multimedia learning environments.
ContributorsSiegle, Robert Franklin (Author) / Craig, Scotty D (Thesis advisor) / Cooke, Nancy J (Committee member) / Nelson, Brian C (Committee member) / Arizona State University (Publisher)
Created2022