Semantic Chat: Enabling Greater Believability through Voice Avatars in Multiplayer and Story-Driven Games
Abstract
A majority of games keep to discrete inputs and have not easily realized the expressivity of spoken language interfaces. Furthermore, natural language processing systems had limitations understanding language intent. For this paper, we define a type of language interface, Semantic Chat, and the challenges of achieving this functionality for interactive fiction and multiplayer games. In the past, games accepted text chat, through a keyboard, or voice chat, through a microphone; however, the inputs were often read verbatim and, at most, pattern matched to a desired intent. With recent advancements in deep learning, language models are able to more effectively derive the semantic meaning behind the textual input, and machine learning models have become increasingly better at transcribing voice. Even so, Semantic Chat is still rarely found in games. In practice, the application of these neural language models is an open problem, with non-trivial challenges in deployment. Using techniques like transfer learning, we discuss the obstacles in realizing believable voice avatars.
Research Areas
