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基于人工智能的语音助手的可用性评估:以亚马逊Alexa为例。

Usability Evaluation of Artificial Intelligence-Based Voice Assistants: The Case of Amazon Alexa.

作者信息

Zwakman Dilawar Shah, Pal Debajyoti, Arpnikanondt Chonlameth

机构信息

School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.

出版信息

SN Comput Sci. 2021;2(1):28. doi: 10.1007/s42979-020-00424-4. Epub 2021 Jan 11.

Abstract

Currently, the use of voice-assistants has been on the rise, but a user-centric usability evaluation of these devices is a must for ensuring their success. System Usability Scale (SUS) is one such popular usability instrument in a Graphical User Interface (GUI) scenario. However, there are certain fundamental differences between GUI and voice-based systems, which makes it uncertain regarding the suitability of SUS in a voice scenario. The present work has a twofold objective: to check the suitability of SUS for usability evaluation of voice-assistants and developing a subjective scale in line with SUS that considers the unique aspects of voice-based communication. We call this scale as the Voice Usability Scale (VUS). For fulfilling the objectives, a subjective test is conducted with 62 participants. An Exploratory Factor Analysis suggests that SUS has a number of drawbacks for measuring the voice usability. Moreover, in case of VUS, the most optimal factor structure identifies three main components: usability, affective, and recognizability and visibility. The current findings should provide an initial starting point to form a useful theoretical and practical basis for subjective usability assessment of voice-based systems.

摘要

目前,语音助手的使用一直在增加,但要确保这些设备取得成功,以用户为中心对其进行可用性评估是必不可少的。系统可用性量表(SUS)是图形用户界面(GUI)场景中一种很受欢迎的可用性工具。然而,GUI系统和基于语音的系统之间存在一些根本差异,这使得SUS在语音场景中的适用性存在不确定性。本研究有两个目标:检验SUS对语音助手可用性评估的适用性,并开发一个与SUS一致的主观量表,该量表考虑了基于语音通信的独特方面。我们将这个量表称为语音可用性量表(VUS)。为实现这些目标,对62名参与者进行了一项主观测试。探索性因素分析表明,SUS在测量语音可用性方面存在一些缺点。此外,对于VUS,最优化的因素结构确定了三个主要组成部分:可用性、情感性以及可识别性和可见性。当前的研究结果应为基于语音的系统的主观可用性评估形成有用的理论和实践基础提供一个初步起点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/219a/7798382/3c846aaaf3a2/42979_2020_424_Fig1_HTML.jpg

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