Reconsidering the voice effect when learning from a virtual human

Highlights

Transfer improved when learners used a pedagogical agent with a modern voice engine.
Participants rated an agent with a modern voice engine just a credible as one with a human voice.
Facilitate learning ratings were identical when an agent had modern voice engine or a human voice.
Better training efficiency was observed when learners used an agent with a modern voice engine.

Abstract

The current paper investigates an essential design component of virtual humans, the voice they communicate with, by examining the impact of varied voice types. A standard voice effect has held that human voices should be paired with virtual humans. The current study revisits this effect. In a randomized trial, virtual humans used one of three voice types (classic and modern text-to-speech engines, as well as human voice) to present information to a sample of participants from an online population. The impact of each voice type on learning, cognitive load, and perceptions of the virtual human were examined. The study found that the modern voice engine produced significantly more learning on transfer outcomes, had greater training efficiency, and was rated at the same level as an agent with a human voice for facilitating learning and credibility while outperforming the older speech engine. These results call into question previous results using older voice engines and the claims of the voice effect.

Keywords

Virtual humans
Pedagogical agent
Voice effect
Multimedia learning
Synthetic voices
Craig S D, Schroeder N L. Reconsidering the voice effect when learning from a virtual human[J]. Computers & Education, 2017, 114:193-205.