Developing a Personality Model for Speech-based Conversational Agents Using the Psycholexical Approach

要旨

We present the first systematic analysis of personality dimensions developed specifically to describe the personality of speech-based conversational agents. Following the psycholexical approach from psychology, we first report on a new multi-method approach to collect potentially descriptive adjectives from 1) a free description task in an online survey (228 unique descriptors), 2) an interaction task in the lab (176 unique descriptors), and 3) a text analysis of 30,000 online reviews of conversational agents (Alexa, Google Assistant, Cortana) (383 unique descriptors). We aggregate the results into a set of 349 adjectives, which are then rated by 744 people in an online survey. A factor analysis reveals that the commonly used Big Five model for human personality does not adequately describe agent personality. As an initial step to developing a personality model, we propose alternative dimensions and discuss implications for the design of agent personalities, personality-aware personalisation, and future research.

受賞
Honorable Mention
キーワード
Big 5
Conversational agents
Personality
著者
Sarah Theres Völkel
Ludwig Maximilian University of Munich, Munich, Germany
Ramona Schödel
Ludwig Maximilian University of Munich, Munich, Germany
Daniel Buschek
University of Bayreuth, Bayreuth, Germany
Clemens Stachl
Stanford University, Palo Alto, CA, USA
Verena Winterhalter
Ludwig Maximilian University of Munich, Munich, Germany
Markus Bühner
Ludwig Maximilian University of Munich, Munich, Germany
Heinrich Hussmann
Ludwig Maximilian University of Munich, Munich, Germany
DOI

10.1145/3313831.3376210

論文URL

https://doi.org/10.1145/3313831.3376210

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: Voice & speech interaction

Paper session
306AB
5 件の発表
2020-04-29 01:00:00
2020-04-29 02:15:00
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