Turn-taking as a design principle for barge-in in Spoken Language Systems

Heins, Rebecca; Franzke, Marita; Durian, Michael; Bayya, Aruna

doi:10.1007/bf02208827

Cited by 13 publications

(3 citation statements)

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“…It was further observed that, at the start of the interview, patients sometimes answered before the robot was finished speaking. This well-known barge-in effect occurred despite the fact that the robot had instructed patients to wait for the blue bar to appear at the top of the tablet before speaking [ 38 ]. Most patients learned after three or four questions that it was better to wait a short while before answering, as they would otherwise have to repeat their answers.…”

Section: Resultsmentioning

confidence: 99%

Quality of Care Perceived by Older Patients and Caregivers in Integrated Care Pathways With Interviewing Assistance From a Social Robot: Noninferiority Randomized Controlled Trial

Boumans¹,

Meulen²,

Aalst³

et al. 2020

J Med Internet Res

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Background Society is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem. Objective Our objective is to evaluate the quality of care perceived by patients and caregivers for an integrated care pathway in an outpatient clinic using a social robot for patient-reported outcome measure (PROM) interviews versus the currently used professional interviews. Methods A multicenter, two-parallel-group, nonblinded, randomized controlled trial was used to test for noninferiority of the quality of care delivered through robot-assisted care. The randomization was performed using a computer-generated table. The setting consisted of two outpatient clinics, and the study took place from July to December 2019. Of 419 patients who visited the participating outpatient clinics, 110 older patients met the criteria for recruitment. Inclusion criteria were the ability to speak and read Dutch and being assisted by a participating health care professional. Exclusion criteria were serious hearing or vision problems, serious cognitive problems, and paranoia or similar psychiatric problems. The intervention consisted of a social robot conducting a 36-item PROM. As the main outcome measure, the customized Consumer Quality Index (CQI) was used, as reported by patients and caregivers for the outpatient pathway of care. Results In total, 75 intermediately frail older patients were included in the study, randomly assigned to the intervention and control groups, and processed: 36 female (48%) and 39 male (52%); mean age 77.4 years (SD 7.3), range 60-91 years. There was no significant difference in the total patient CQI scores between the patients included in the robot-assisted care pathway (mean 9.27, SD 0.65, n=37) and those in the control group (mean 9.00, SD 0.70, n=38): P=.08, 95% CI –0.04 to 0.58. There was no significant difference in the total CQI scores between caregivers in the intervention group (mean 9.21, SD 0.76, n=30) and those in the control group (mean 9.09, SD 0.60, n=35): P=.47, 95% CI –0.21 to 0.46. No harm or unintended effects occurred. Conclusions Geriatric patients and their informal caregivers valued robot-assisted and nonrobot-assisted care pathways equally. Trial Registration ClinicalTrials.gov NCT03857789; https://clinicaltrials.gov/ct2/show/NCT03857789

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Section: Resultsmentioning

confidence: 99%

Quality of Care Perceived by Older Patients and Caregivers in Integrated Care Pathways With Interviewing Assistance From a Social Robot: Noninferiority Randomized Controlled Trial

Boumans¹,

Meulen²,

Aalst³

et al. 2020

J Med Internet Res

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“…This first of all required us to classify interruptions and responses into different types. Although we found a large body of work focused on the phenomenon of interrupting someone (what interruptions are [11], how people take turns [38,39], overlapping/simultaneous speech [40,12], functions of interruptions [41], the psychology of interrupting [42,43], control of topic in conversation [44], effects of barge-in on ASR [45], prosodic cues of interruptions [46]), work addressing the problem of how a participant in a conversation approaches responding to an interruption was not much in evidence.…”

Section: Context-sensitive System Recoveries From User Barge-inmentioning

confidence: 99%

Generating context-sensitive ECA responses to user barge-in interruptions

Crook

Field

Smith

et al. 2012

J Multimodal User Interfaces

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“…Giving the agent the capacity to interrupt human users or to respond in time to an interruption helps to improve the quality of interaction and to increase the engagement in the conversation [31]. Thus, the agent should be able to observe human signals and make its own decisions about when and how to interrupt, for example human tends to interrupt more at syntactic and prosodic boundaries [13]. Likewise, when interrupted by human, the agent should be able to recognize the type of interruption and replan its own behaviour on the fly.…”

Section: Introductionmentioning

confidence: 99%

What If I Interrupt You

Liu

2021

Proceedings of the 2021 International Conference on Multimodal Interaction

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Today, with the development of science and technology, we hope to improve human-agent interaction, agents will become more 'human-like', so that they communicate autonomously with humans both verbally and non-verbally. A challenge for the Embodied Conversational Agent is then to handle and manage speaking turn exchanges, and, more particularly interruptions, these are inherent in human-human interaction. We present our ongoing work on modeling interruption management in human-agent interaction. Our research contains two main aspects: 1) when and how should the virtual agent interrupt human users, and 2) how should the virtual agent respond when being interrupted by human users. To achieve this goal, we first started by analyzing human-human interaction data. CCS CONCEPTS• Human-centered computing → Human agent interaction (HAI).

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Turn-taking as a design principle for barge-in in Spoken Language Systems

Cited by 13 publications

References 1 publication

Quality of Care Perceived by Older Patients and Caregivers in Integrated Care Pathways With Interviewing Assistance From a Social Robot: Noninferiority Randomized Controlled Trial

Quality of Care Perceived by Older Patients and Caregivers in Integrated Care Pathways With Interviewing Assistance From a Social Robot: Noninferiority Randomized Controlled Trial

Generating context-sensitive ECA responses to user barge-in interruptions

What If I Interrupt You

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