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Deep Learning with Virtual Agents : = How Accented and Synthetic Voices Affect Outcomes.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning with Virtual Agents :/
Reminder of title:	How Accented and Synthetic Voices Affect Outcomes.
Author:	Siegle, Robert Franklin.
Description:	1 online resource (75 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
Subject:	Psychology. -
Online resource:	click for full text (PQDT)
ISBN:	9798841780601

Deep Learning with Virtual Agents : = How Accented and Synthetic Voices Affect Outcomes.
Siegle, Robert Franklin.

Deep Learning with Virtual Agents :How Accented and Synthetic Voices Affect Outcomes. - 1 online resource (75 pages)

Source: Masters Abstracts International, Volume: 84-03.

Thesis (M.S.)--Arizona State University, 2022.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798841780601Subjects--Topical Terms:

555998
Psychology.
Subjects--Index Terms:

AccentIndex Terms--Genre/Form:

554714
Electronic books.

Deep Learning with Virtual Agents : = How Accented and Synthetic Voices Affect Outcomes.
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504 $a Includes bibliographical references
520 $a 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.
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