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基于停顿检测的面向日常健康监测的情景对话系统。

Scenario-based dialogue system based on pause detection toward daily health monitoring.

作者信息

Kumagai Kazumi, Tokunaga Seiki, Miyake Norihisa P, Tamura Kazuhiro, Mizuuchi Ikuo, Otake-Matsuura Mihoko

机构信息

Center for Advanced Intelligence Project, RIKEN, Nihonbashi 1-chome Mitsui Building, 15th floor, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan.

Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588, Japan.

出版信息

J Rehabil Assist Technol Eng. 2022 Oct 13;9:20556683221133367. doi: 10.1177/20556683221133367. eCollection 2022 Jan-Dec.

Abstract

INTRODUCTION

We have conducted research on building a robot dialogue system to support the independent living of older adults. In order to provide appropriate support for them, it is necessary to obtain as much information, particularly related to their health condition, as possible. As the first step, we have examined a method to allow dialogue to continue for longer periods.

METHODS

A scenario-based dialogue system utilizing pause detection for turn-taking was built. The practicality of adjusting the system based on the dialogue rhythm of each individual was studied. The system was evaluated through user studies with a total of 20 users, 10 of whom were older adults.

RESULTS

The system detected pauses in the user's speech using the sound level of their voice, and predicted the duration and number of pauses based on past dialogue data. Thus, the system initiated the robot's voice-call after the user's predicted speech.

CONCLUSIONS

Multiple turns of dialogue between robot and older adults are found possible under the system, despite several overlaps of robot's and users' speech observed. The users responded to the robot, including the questions related to health conditions. The feasibility of a scenario-based dialogue system was suggested; however, improvements are required.

摘要

引言

我们开展了关于构建机器人对话系统以支持老年人独立生活的研究。为了给他们提供适当的支持,有必要获取尽可能多的信息,尤其是与他们健康状况相关的信息。作为第一步,我们研究了一种使对话能够持续更长时间的方法。

方法

构建了一个基于场景的对话系统,该系统利用停顿检测来进行话轮转换。研究了根据每个人的对话节奏调整系统的实用性。通过对总共20名用户进行用户研究对该系统进行评估,其中10名是老年人。

结果

该系统利用用户语音的音量检测用户语音中的停顿,并根据过去的对话数据预测停顿的持续时间和数量。因此,该系统在用户预测的讲话之后启动机器人的语音呼叫。

结论

尽管观察到机器人和用户的语音有几次重叠,但在该系统下机器人与老年人之间进行多轮对话是可能的。用户对机器人做出了回应,包括与健康状况相关的问题。基于场景的对话系统的可行性得到了证明;然而,仍需要改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c94c/9578174/5cb26da24ce4/10.1177_20556683221133367-fig1.jpg

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