Large David R, Clark Leigh, Quandt Annie, Burnett Gary, Skrypchuk Lee
Human Factors Research Group, Faculty of Engineering, University of Nottingham, UK.
Human Factors Research Group, Faculty of Engineering, University of Nottingham, UK.
Appl Ergon. 2017 Sep;63:53-61. doi: 10.1016/j.apergo.2017.04.003. Epub 2017 Apr 12.
Given the proliferation of 'intelligent' and 'socially-aware' digital assistants embodying everyday mobile technology - and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices - it appears inevitable that next generation vehicles will be embodied by digital assistants and utilise spoken language as a method of interaction. From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis - formulating responses; turn-taking; back-channelling, fillers and hesitation; vague language; mitigating requests and politeness and praise. The results can be used to inform the design of future in-vehicle natural language systems, in particular to help manage the tension between designing for an engaging dialogue (important for technology acceptance) and designing for an effective dialogue (important to minimise distraction in a driving context).
鉴于体现日常移动技术的“智能”和“具备社交意识”的数字助理大量涌现，以及在汽车中使用语音激活控制和界面可减少与车内设备交互时的视觉和手动干扰这一不可否认的逻辑，下一代车辆似乎不可避免地将由数字助理主导，并将口语作为一种交互方式。从设计角度来看，确定数字驾驶助理应采用的语言和交互风格取决于它们在所处的社会结构和背景中所扮演的角色。因此，我们进行了一项定性的“奥兹巫师”研究，以探索驾驶员可能如何与自然语言数字驾驶助理进行语言交互。25名参与者在中等保真度的驾驶模拟器中驾驶10分钟，同时与一个先进的、高性能的对话式数字驾驶助理进行交互。所有交流内容都被转录，并使用通常用于人际调查的公认语言技术（如话语和对话分析）进行分析。语言使用模式表明，与数字助理的交互本质上是社会性的，参与者赋予助理平等的社会地位和高级认知处理能力。例如，参与者很有礼貌，在对话中积极控制话轮转换，并使用反馈、填充词和犹豫表达，就像在人际交流中一样。此外，参与者期望数字助理能够理解并处理用模糊词和表达缓和的复杂请求，这些请求还夹杂着模糊语言和指示性参照，需要共享上下文信息和相互理解。研究结果呈现为分析过程中出现的六个主题——制定回应；话轮转换；反馈、填充词和犹豫；模糊语言；缓和请求以及礼貌和赞扬。这些结果可用于为未来的车载自然语言系统设计提供参考，特别是有助于在为引人入胜的对话进行设计（对技术接受度很重要）和为有效对话进行设计（对在驾驶环境中尽量减少干扰很重要）之间进行权衡。
