Clarifying False Memories in Voice-based Search

Kiesel, Johannes; Bahrami, Arefeh; Stein, Benno; Anand, Avishek; Hagen, Matthias

doi:10.1145/3295750.3298961

Cited by 5 publications

(7 citation statements)

References 20 publications

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“…They also noticed that the effectiveness of query clarification depends on the number and lengths of the possible answers. In a follow-up, Kiesel et al (2019) showed that clarifications usually improve user satisfaction and that satisfaction is significantly impacted by the tone of voiced clarifications. Zhang et al (2018) proposed the System Ask-User Respond paradigm for conversational search.…”

Section: Human Interaction With Systemsmentioning

confidence: 99%

Analyzing clarification in asynchronous information‐seeking conversations

Tavakoli

Zamani

Scholer

et al. 2021

Asso for Info Science & Tech

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This research analyzes human-generated clarification questions to provide insights into how they are used to disambiguate and provide a better understanding of information needs. A set of clarification questions is extracted from posts on the Stack Exchange platform. Novel taxonomy is defined for the annotation of the questions and their responses. We investigate the clarification questions in terms of whether they add any information to the post (the initial question posted by the asker) and the accepted answer, which is the answer chosen by the asker. After identifying, which clarification questions are more useful, we investigated the characteristics of these questions in terms of their types and patterns. Non-useful clarification questions are identified, and their patterns are compared with useful clarifications. Our analysis indicates that the most useful clarification questions have similar patterns, regardless of topic. This research contributes to an understanding of clarification in conversations and can provide insight for clarification dialogues in conversational search scenarios and for the possible system generation of clarification requests in information-seeking conversations.

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Section: Human Interaction With Systemsmentioning

confidence: 99%

Analyzing clarification in asynchronous information‐seeking conversations

Tavakoli

Zamani

Scholer

et al. 2021

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Continued use [33]; Satisfaction [33]; System efectiveness [24]; Tone clarity [8]; Satisfaction [3]; System Usability Scale (SUS) [6,44]; Speech User Interface Service Quality Questionnaire (SUISQ-R) [6]; Document feedback [38]; Voice understandability [10]; Perception of rapport [46]; Group decision performance [46]; Voice performance [11] Social presence [32,33]; Intimacy [25,33]; Closeness (Subjective closeness index) [53];…”

Section: Ivs and IV Frameworkmentioning

confidence: 99%

“…66% (21) used a between-subjects design, and 34% (11) used within. 77% (24) were lab-based, 3 were in the field, and 4 were other (e.g., questionnaires). 59% (17) involved voice assistants, with the rest involving conversational agents (5), computer voice (2), in-car assistants (2), and three others (website, chatbot, and speech interface).…”

Section: Description Of Studiesmentioning

confidence: 99%

“…Only Form Factor and Voice Characteristics involved field work. [39]; Exercise Behavior [37]; Gaze [16]; Language production (e.g., lexical complexity, adaptation) [51]; Interaction time [27]; Driving performance [38]; Disclosure (user responses) [53]; Accidents [41] Subj Continued use [33]; Satisfaction [33]; System effectiveness [24]; Tone clarity [8]; Satisfaction [3]; System Usability Scale (SUS) [6,44]; Speech User Interface Service Quality Questionnaire (SUISQ-R) [6]; Document feedback [38]; Voice understandability [10]; Perception of rapport [46]; Group decision performance [46]; Voice performance [11]; MeCue questionnaire [4] Engagement None Obj Interaction behavior (e.g., frequency and time) [37]; Emotional engagement (facial expressions) [47]…”

Section: Interaction Modalitymentioning

confidence: 99%

“…Trust [7,25,33]; Trustworthiness [30,46]; Safety [38] Likeability Obj None Subj Likeability [4,7,8,21,24]; Voice attractiveness [30]; Voice liking [11]; Information liking [11]; Attractiveness (UEQ modules) [4] Desirability (Marlowe-Crowne Social Desirability Scale -MCSDS) [10] Preference Obj None Subj Preference (self-reported) [33,38,46]; Smile-o-meter [52] Acceptance Obj None Subj Acceptance (self-reported) [7]; Acceptance Scale [4]; Human-likeness [13]; Attractiveness differences [6]; Self-efficiency [7]; Interaction questionnaire [19]; Godspeed questionnaire [19]…”

Section: Subjmentioning

confidence: 99%

See 2 more Smart Citations

Measuring Voice UX Quantitatively

Seaborn

Urakami

2021

Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems

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Computer voice is experiencing a renaissance through the growing popularity of voice-based interfaces, agents, and environments. Yet, how to measure the user experience (UX) of voice-based systems remains an open and urgent question, especially given that their form factors and interaction styles tend to be non-visual, intangible, and often considered disembodied or "body-less. " As a frst step, we surveyed the ACM and IEEE literatures to determine which quantitative measures and measurements have been deemed important for voice UX. Our fndings show that there is little consensus, even with similar situations and systems, as well as an overreliance on lab work and unvalidated scales. In response, we ofer two high-level descriptive frameworks for guiding future research, developing standardized instruments, and informing ongoing review work. Our work highlights the current strengths and weaknesses of voice UX research and charts a path towards measuring voice UX in a more comprehensive way. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Human computer interaction (HCI); Interaction devices; Sound-based input / output.

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Understanding Voice Search Behavior: Review and Synthesis of Research

Xing

Yuan

et al. 2020

Lecture Notes in Computer Science

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Clarifying False Memories in Voice-based Search

Cited by 5 publications

References 20 publications

Analyzing clarification in asynchronous information‐seeking conversations

Analyzing clarification in asynchronous information‐seeking conversations

Measuring Voice UX Quantitatively

Understanding Voice Search Behavior: Review and Synthesis of Research

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