A comparative study of speech in the call center

Suhm, Bernhard; Bers, Josh; McCarthy, Dan; Freeman, Barbara; Getty, David J.; Godfrey, Katherine; Peterson, Pat

doi:10.1145/503376.503427

Cited by 28 publications

(5 citation statements)

References 7 publications

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“…Questionnaires were used in the majority of papers (N=40), assessing aspects such as user attitudes (e.g. Suhm et al, 2002) and usability (e.g. Perugini, Anderson, & Moroney, 2007).…”

Section: Data Collection Methodsmentioning

confidence: 99%

“…Litman & Pan (2002) evaluated an online train schedule retrieval system accessed via telephone, revealing performance improvements when the system predicts ASR problems and adapts to more conservative dialogue strategies. In another paper, a field study compared call routing in a cell center environment (Suhm et al, 2002). The results showed improvements in usability for natural language call routing compared to traditional touch-tone menus, as well as the potential for reducing call center costs.…”

Section: Experiences With Ivrsmentioning

confidence: 99%

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

Clark

Doyle

Garaialde

et al. 2019

Interacting With Computers

147

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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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“…Questionnaires were used in the majority of papers (N=40), assessing aspects such as user attitudes (e.g. Suhm et al, 2002) and usability (e.g. Perugini, Anderson, & Moroney, 2007).…”

Section: Data Collection Methodsmentioning

confidence: 99%

Section: Experiences With Ivrsmentioning

confidence: 99%

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

Clark

Doyle

Garaialde

et al. 2019

Interacting With Computers

147

View full text Add to dashboard Cite

show abstract

“…Moreover, it provides a more human-like kind of interaction, but also the ASR-based systems show several problems that make the experience worse: frequent misunderstandings, standardized answers, difficulty to face unexpected situations, etc. as discussed in Suhm et al (2002); Bocklund and Bengtson (2002). The consequence is the perception of a paradox: a system with the aim of reproducing a conversational style of interaction which often fail in meeting the user's expectations, such as avoiding the boring repetition of instructions.…”

Section: Introductionmentioning

confidence: 96%

Adapting the interaction in a call centre system

Cena

Torre

2006

Interacting with Computers

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This paper describes Adaptive Response and Routing System (ARRS), a system which personalizes the management of the entire answering process in a Call Centre infrastructure. The system is characterized by combining two kinds of adaptation: to the caller and to the operator. The aim is to support the user incrementally, improving the global quality of the interaction. In particular, the system combines a first process of automatic and personalized response with a second one of adaptive routing of the call to the operator who best fits the caller's features, according to a usercentered workflow. The results of a preliminary evaluation confirm the validity of the approach. q

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“…Through speech recognition in the AI system, customers can tell the system which kinds of services they require; on this basis, they are directly routed to certain services (Tang et al., 2003). In contrast, the IVR system typically relies on a hierarchical structure that directs customers in a step‐by‐step manner to locate specific services (Suhm et al., 2002). To switch services, customers are required to return to the main service menu and repeat the above‐mentioned actions to select another service.…”

Section: Introductionmentioning

confidence: 99%

Voice‐based AI in call center customer service: A natural field experiment

Wang

Huang

Hong

et al. 2023

Production and Operations Management

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Voice-based artificial intelligence (AI) systems have been recently deployed to replace traditional interactive voice response (IVR) systems in call center customer service. However, there is little evidence that sheds light on how the implementation of AI systems impacts customer behavior, as well as AI systems' effects on call center customer service performance. By leveraging the proprietary data obtained from a natural field experiment in a large telecommunication company, we examine how the introduction of a voice-based AI system affects call length, customers' demand for human service, and customer complaints in call center customer service. We find that the implementation of the AI system temporarily increases the duration of machine service and customers' demand for human service; however, it persistently reduces customer complaints. Furthermore, our results reveal interesting heterogeneity in the effectiveness of the voice-based AI system. For relatively simple service requests, the AI system reduces customer complaints for both experienced and inexperienced customers. However, for complex requests, customers appear to learn from the prior experience of interacting with the AI system, which leads to fewer complaints. Moreover, the AI-based system has a significantly larger effect on reducing customer complaints for older and female customers as well as for customers who have had extensive experience using the IVR system. Finally, we find that speech-recognition failures in customer-AI interactions lead to increases in customers' demand for human service and customer complaints. The results from this study provide implications for the implementation of an AI system in call center operations.

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A comparative study of speech in the call center

Cited by 28 publications

References 7 publications

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

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

Adapting the interaction in a call centre system

Voice‐based AI in call center customer service: A natural field experiment

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