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