Exploring Effects of Conversational Fillers on User Perception of Conversational Agents

Jeong, Yuin; Lee, Juho; Kang, Younah

doi:10.1145/3290607.3312913

Cited by 19 publications

(13 citation statements)

References 10 publications

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“…Ohta et al [141] found that pauses and silences added at natural breaks within sentences improved comprehension of the information presented by an agent as well as the naturalness of the agent's voice. In contrast, Jeong et al [84] found that likeability when vocal fillers were used depended on the context, specifically better for social situations rather than in a service context. In general, it seems that vocal fillers improve experience with agents.…”

Section: Vocalmentioning

confidence: 91%

Voice in Human–Agent Interaction

Seaborn

Miyake

Pennefather³

et al. 2021

ACM Comput. Surv.

106

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Social robots, conversational agents, voice assistants, and other embodied AI are increasingly a feature of everyday life. What connects these various types of intelligent agents is their ability to interact with people through voice. Voice is becoming an essential modality of embodiment, communication, and interaction between computer-based agents and end-users. This survey presents a meta-synthesis on agent voice in the design and experience of agents from a human-centered perspective: voice-based human--agent interaction (vHAI). Findings emphasize the social role of voice in HAI as well as circumscribe a relationship between agent voice and body, corresponding to human models of social psychology and cognition. Additionally, changes in perceptions of and reactions to agent voice over time reveals a generational shift coinciding with the commercial proliferation of mobile voice assistants. The main contributions of this work are a vHAI classification framework for voice across various agent forms, contexts, and user groups, a critical analysis grounded in key theories, and an identification of future directions for the oncoming wave of vocal machines.

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Section: Vocalmentioning

confidence: 91%

Voice in Human–Agent Interaction

Seaborn

Miyake

Pennefather³

et al. 2021

ACM Comput. Surv.

106

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“…We used Amazon Polly (a text-to-speech system) to avoid potential problems with accented speech: Such speech is not only more difficult to comprehend (Crowther et al, 2016), but an accented speaker is also judged as less credible (Lev-Ari & Keysar, 2010). Previous research utilising Amazon Polly observed promising results in terms of use Amazon Polly in behavioural research, suggesting that artificial voices sound quite naturally (Jeong et al, 2019), and are rated closely to real human speaker (Cambre et al, 2020). Despite these promising results, some dose of wary has to be adopted.…”

Section: Methodsmentioning

confidence: 99%

The moral foreign language effect is stable across presentation modalities

Muda

Pieńkosz

Francis

et al. 2020

Quarterly Journal of Experimental Psychology

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People’s judgements and decisions often change when made in their foreign language. Existing research testing this foreign language effect has predominantly used text-based stimuli with little research focusing on the impact of listening to audio stimuli on the effect. The only existing study on this topic found shifts in people’s moral decisions only in the audio modality. First, by reanalysing the data from this previous study and by collecting data in an additional experiment, we found no consistent effects of using foreign language on moral judgements. Second, in both data sets, we found no significant language by modality interaction. Overall, our results highlight the need for more robust testing of the foreign language effect, and its boundary conditions. However, modality of presentation does not appear to be a candidate for explaining its variability. Data and materials for this experiment are available at https://osf.io/qbjxn/ .

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“…Additionally, our findings indicated some evidence regarding the influence of interaction design elements as determinants of rapport between the user and IA. Researchers studied two variables, mode (9 OBS, e.g., Miehle et al, 2018) and degree of freedom (1 OBS, Jeong et al, 2019). The influence of mode was found to be significant.…”

Section: Independent Variables On Relational Elementsmentioning

confidence: 99%

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

et al. 2022

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Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

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Exploring Effects of Conversational Fillers on User Perception of Conversational Agents

Cited by 19 publications

References 10 publications

Voice in Human–Agent Interaction

Voice in Human–Agent Interaction

The moral foreign language effect is stable across presentation modalities

Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future

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