Identifying Personality Dimensions for Characters of Digital Agents

Braun, Michael; Alt, Florian

doi:10.1007/978-3-030-15954-2_8

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…The Barcelona Conference on Education 2022 Official Conference Proceedings ISSN: 2435-9467 content and possible, future push-messaging with learning tips was more critical. The positive perception of the chatbot's vivid character and appealing design confirmed the intention to create a more personalized conversation experience using a chatbot persona (Braun & Alt, 2020). According to the participants' feedback, content should be more specific and accurate, yet still short.…”

Section: Discussionmentioning

confidence: 76%

“…In the second phase, the chatbot will be trained for AI-based answer generation to deal with individual (non-standard) questions based on a self-uploading knowledge base. The chatbot's design, character traits, and conversational tone were based on a pre-developed persona, which is supposed to create a more personalized conversation experience (Braun & Alt, 2020). Apart from answering standard and individual questions from students and prospective students, the chatbot will also share unrequested information such as exam reminders and learning advice (e.g., push messages) in the subsequent development stage and will therefore be not only responsive, yet also proactive.…”

Section: Informational Componentmentioning

confidence: 99%

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The Barcelona Conference on Education 2022: Official Conference Proceedings

2022

BCE Official Conference Proceedings

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Section: Discussionmentioning

confidence: 76%

Section: Informational Componentmentioning

confidence: 99%

The Barcelona Conference on Education 2022: Official Conference Proceedings

2022

BCE Official Conference Proceedings

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“…In this study, we used videos in which DSA speaks utterances as experimental stimuli. In addition, although there have been studies to add human-like personalities to DSAs [15], the purpose and approach are different from this study, which focuses on the personality characteristics of users.…”

Section: User Personality and Acceptability Of Agentsmentioning

confidence: 99%

Should a Driving Support Agent Provide Explicit Instructions to the User? Video-based Study Focused on Politeness Strategies

Katagami

Tanaka

Kanamori

et al. 2021

Proceedings of the 9th International Conference on Human-Agent Interaction

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Soon, autonomous cars are expected to become widespread, but at present, it is still common for people to drive cars manually. A driving support agent (DSA) is used to support driving using the human-agent interaction approach. In previous research, it was shown that DSAs can be useful in assisting the user through voice utterances. However, previous studies focusing on DSA utterance design have not compared off-record strategies with other politeness strategies that explicitly communicate the speaker's intentions, and it was not clear whether DSA should provide explicit utterances to the user. Therefore, in this study, a video-based subjective evaluation experiment (n=240) was conducted to compare the acceptability of off-record strategies, positive politeness strategies, negative politeness strategies, and direct utterances without politeness strategies. The results of the experiment showed that the negative politeness strategy was evaluated significantly higher than the off-record strategy on evaluation items related to functionality. This result suggests the usefulness of providing explicit instructions with linguistic consideration (politeness) when DSA provides driving assistance to users. In addition to the above findings, there were several correlations between the user's personality characteristics and the subjective evaluation of the DSA. Specifically, there was a significant positive correlation between the level of conscientiousness of the user and the direct utterance evaluation value. For the positive politeness strategy and off-record strategy, there were no significant correlations between evaluation categories of the dislikeability and users' personality characteristics. These results suggest that individual differences in negative impressions due to personality characteristics are small in the positive politeness and off-record strategies.

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“…The importance of Personality Perception (PP) from speech has increased with the advancement of science and technology, since in today's world of digital and cyberspace, analyzing individuals' perceived personality (not true personality) can help to grow these spaces smarter [1]. Regarding the field of the personality-related humancomputer interface [2], a wide range of interesting applications, such as in-car voice assistants [3], personalization of user interfaces [4], user-problematic behaviors in cyberspace [5], troubleshooting a cold start problem for new users of virtual service provider systems [6] has been developed over the recent years.…”

Section: Introductionmentioning

confidence: 99%

“…This phenomenon deteriorates the model's performance through irrelevant features [20]. 3. By increasing the number of features, the trainable model parameters increase, and more samples would be needed for the complete training process as a result.…”

Section: Introductionmentioning

confidence: 99%

Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

Zaferani¹,

Teshnehlab²,

Vali³

2021

IEEE Access

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On account of an increase in the human-computer interface applications, the study of automatic personality perception has become more and more prevalent than speech signal processing in recent years. These studies have shown that personality traits derived from psychology theories mainly affect acoustic features. However, some obstacles remain in the automatic personality perception classification, and the most important one is to extract the features related to each personality trait. Previous studies have shown that the personality effect differs from one acoustic feature to the others. Additionally, there are many features one can extract from speech signals. Curse of dimensionality in features also makes the classification difficult. This paper aimed to introduce and examine a novel and efficient automatic feature extraction method to classify the well-known big five personality traits. In this regard, three data augmentation methods for increasing data samples were examined. Afterwards, 6,373 statistical features were extracted from the nonverbal features of the SSPNet Speaker Personality Corpus. Finally, an innovative stacked asymmetric auto-encoder was utilized to extract useful features automatically to improve classification results. Compared with the conventional stacked auto-encoder and convolutional neural network, the proposed method exhibited an average improvement of 12.40%(10.14%) and 14.36%(1.42%) in terms of the unweighted average recall (accuracy), respectively. In comparison with other published works, classification results also revealed a notable average enhancement (11.78%) for unweighted average recall for all five traits and an average improvement of 5.1% for accuracy in two out of five personality traits.

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Identifying Personality Dimensions for Characters of Digital Agents

Cited by 10 publications

References 35 publications

The Barcelona Conference on Education 2022: Official Conference Proceedings

The Barcelona Conference on Education 2022: Official Conference Proceedings

Should a Driving Support Agent Provide Explicit Instructions to the User? Video-based Study Focused on Politeness Strategies

Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

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