The impact of voice characteristics on user response in an interactive voice response system

Evans, Rochelle E.; Kortum, Philip

doi:10.1016/j.intcom.2010.07.001

Cited by 25 publications

(20 citation statements)

References 43 publications

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“…However, during our analysis of these calls we found no signs of inhibition like non-disclosure of name and location, or hesitation in recording the message. These results are similar to those obtained by Evans and Kortum [7], where disclosure rates were not affected by prompt gender or personality in an IVR system deployed in a medical setting. As a result, we chose not to explore the impact of prompt gender and familiarity in greater detail.…”

Section: Content Analysis Of Good Callssupporting

confidence: 89%

PhonePeti

Koradia

Seth

2012

Proceedings of the Fifth International Conference on Information and Communication Technologies and Development

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Community Radio (CR) stations are short range radio stations that serve the local media needs of their surrounding communities. Community participation by way of helping set the station agenda, airing of people's voices, and providing them with a local communication medium, is the defining feature of CR. But this philosophy has been hard to execute in practice because of logistical difficulties, with station staff not being able to reach out to a listenership-base spread across several hundreds of square kilometers. In today's context though, the high penetration of mobile phones has made it easier for listeners to participate in the running of radio stations, but the potential of telephony and radio integration has been exploited only minimally.In this paper, we explore the use of PhonePeti, an automated answering machine system in a community radio station based in Gurgaon, India. Answering machines are one of several ways to bring together the radio and telephony mediums. We show that this alone has the potential to considerably improve community engagement, but it also opens up many interesting issues on usability. Through quantitative and content analysis of 758 calls from 411 callers over two iterations of PhonePeti, combined with telephonic interviews of several callers, we show that significant challenges arise in being able to explain the concept of an answering machine to people who have not been exposed to a similar system in the past. We then show, through call statistics, that PhonePeti has increased community engagement by enabling more listeners to reach the station. Finally, we show that an answering machine system can be used to collect useful information from the callers.

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Section: Content Analysis Of Good Callssupporting

confidence: 89%

PhonePeti

Koradia

Seth

2012

Proceedings of the Fifth International Conference on Information and Communication Technologies and Development

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“…Many studies show that the effect of social presence is stronger in oral interaction than in textual interaction (Nass & Gong, 2000; Sallnäs, 2005). Studies have also shown that users interacting with an interface that uses voice ascribe a personality to it (Evans & Kortum, 2010; Lee & Nass, 2003; Stern, Mullennix, & Yaroslavsky, 2006). Thus, it is reasonable to argue that agents who communicate verbally are perceived as more credible (Lester & Stone, 1997).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

An empirical study on anthropomorphism and engagement with disembodied AIs and consumers' re‐use behavior

Moriuchi

2020

Psychology and Marketing

103

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The integration of artificial intelligence (AI) into the human race has gradually become a norm. AI entails technology assemblages such as machine learning, natural language processing, and reasoning. The influence of AI systems has intensified in consumers' daily lives. Many consumers have interacted with the notions of AI through advertisements or having personal experiences. Many consumers are curious about the use of AI. This paper reports three studies conducted to determine whether anthropomorphism (ANTH) and engagement play a role in consumers' intention to re-use a voice assistant (VA; a machine-learning AI). The second study will determine if ANTH and engagement play a role when consumers are using the VAs for different activities (task completion vs. information gathering). In addition, in Study 2, actual re-use behavior was also tested in the model, which encouraged a stronger overall model fit. The results show that in general effort expectation (EE) has a strong positive impact on consumers' usage experience of the VA. However, between the two types of activities, EE has a stronger impact on consumers' usage experience for information-gathering activities, whereas performance expectation has a stronger impact on usage experience when consumers use the VA for taskcompletion purposes. The third study used internet usage experience as a moderating variable to determine the boundaries of the mediating effects in the study. The results show that the mediators yield results similar to prior studies.

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“…The similarity-attraction effect was also researched by Dahlbäck et al (2007), in which choosing a voice that matches the accent of the user, rather than an accent related to the information being described, led people to view a vocal source as more informative and likeable, overriding any perceived expertise effects. Other research on medical IVR interactions found no effects of either user gender or voice personality conditions on user behaviours such as self-disclosure (Evans & Kortum, 2010), which suggests that manipulating certain voice characteristics may be less impactful in some interaction scenarios than others.…”

Section: System Speech Productionmentioning

confidence: 88%

The State of Speech in HCI: Trends, Themes and Challenges

Clark

Doyle

Garaialde

et al. 2019

Interacting With Computers

145

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Speech interfaces are growing in popularity. Through a review of 68 research papers this work maps the trends, themes, findings and methods of empirical research on speech interfaces in HCI. We find that most studies are usability/theory-focused or explore wider system experiences, evaluating Wizard of Oz, prototypes, or developed systems by using self-report questionnaires to measure concepts like usability and user attitudes. A thematic analysis of the research found that speech HCI work focuses on nine key topics: system speech production, modality comparison, user speech production, assistive technology & accessibility, design insight, experiences with interactive voice response (IVR) systems, using speech technology for development, people's experiences with intelligent personal assistants (IPAs) and how user memory affects speech interface interaction. From these insights we identify gaps and challenges in speech research, notably the need to develop theories of speech interface interaction, grow critical mass in this domain, increase design work, and expand research from single to multiple user interaction contexts so as to reflect current use contexts. We also highlight the need to improve measure reliability, validity and consistency, in the wild deployment and reduce barriers to building fully functional speech interfaces for research. Author Keywords Speech interfaces; speech HCI; review; speech technology; voice user interfaces Research Highlights• Most papers focused on usability/theory-based or wider system experience research with a focus on Wizard of Oz and developed systems, though a lack of design work • Questionnaires on usability and user attitudes often used but few were reliable or validated • Thematic analysis showed nine primary research topics • Gaps in research critical mass, speech HCI theories, and multiple user contexts

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The impact of voice characteristics on user response in an interactive voice response system

Cited by 25 publications

References 43 publications

PhonePeti

PhonePeti

An empirical study on anthropomorphism and engagement with disembodied AIs and consumers' re‐use behavior

The State of Speech in HCI: Trends, Themes and Challenges

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