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Mapping spoken text to gestures is an important research topic for robotswith conversation capabilities. According to studies on human co-speechgestures, a reasonable solution for mapping is using a concept-based approachin which a text is first mapped to a semantic cluster (i.e., a concept)containing texts with similar meanings. Subsequently, each concept is mapped toa predefined gesture. By using a concept-based approach, this paper discussesthe practical issue of obtaining concepts for a unique vocabulary personalizedfor a conversational agent. Using Microsoft Rinna as an agent, we qualitativelycompare concepts obtained automatically through a natural language processing(NLP) approach to those obtained manually through a sociological approach. Wethen identify three limitations of the NLP approach: at the semantic level withemojis and symbols; at the semantic level with slang, new words, and buzzwords;and at the pragmatic level. We attribute these limitations to the personalizedvocabulary of Rinna. A follow-up experiment demonstrates that robot gesturesselected using a concept-based approach leave a better impression than randomlyselected gestures for the Rinna vocabulary, suggesting the usefulness of aconcept-based gesture generation system for personalized vocabularies. Thisstudy provides insights into the development of gesture generation systems forconversational agents with personalized vocabularies.

Labeling the Phrases of a Conversational Agent with a Unique Personalized Vocabulary