ue4 启用ui_如何实现语音的AI启用ui设计原则
ue4 启用ui
支持AI的UI的设计原则 (Design principles for AI-enabled UI)
In my previous articles, I described the design process for AI-enabled user interfaces at Deloitte’s impact foundation project ‘Social Robot Alice.’ I also discovered four baseline design principles to keep in mind when designing for AI-enabled user interfaces:
在我以前的文章中，我在德勤的影响力基础项目“社交机器人爱丽丝”中描述了支持AI的用户界面的设计过程 。 我还发现了在设计具有AI功能的用户界面时要记住的四个基线设计原则 ：
1. discovery and expectation management;
1.发现和期望管理；
2. design for forgiveness;
2.原谅的设计；
3. data transparency and tailoring, and
3.数据透明度和定制，以及
4. privacy, security, and control.
4.隐私，安全和控制。
With this article, I cover how we implemented these design principles in an AI-enabled voice user interface (VUI). (Read about the design principles here). This article clarifies the motives behind chosen design decisions and uncovers other design considerations when designing for an AI-enabled voice user interface such as ‘Social robot Alice.’
通过本文，我将介绍如何在支持AI- 语音用户界面(VUI) 来实现这些设计原则 。 ( 在这里阅读有关设计原理的信息 )。 这篇文章阐明了动机选择的设计决策和揭露其他设计考虑后面当设计启用了AI- 语音用户界面 ，如“社会机器人爱丽丝”。
案例：“社交机器人爱丽丝” (Case: ‘Social robot Alice’)
‘Social Robot Alice’ is a VUI that acts as a social buddy for senior adults facing loneliness, an initiative by VU University, and Deloitte. The robot has two primary users: the caretaker (user A) and the senior adult (user B). The caretaker (user A) configures each robot to be personalized for each senior adult (user B). (Read more about the project here).
“社交机器人爱丽丝”是VUI，它是面对孤独感的老年人的社交伙伴，这是VU大学和德勤发起的一项举措。 机器人有两个主要用户：看护者(用户A)和老年人(用户B)。 管理员(用户A)将每个机器人配置为针对每个成年人(用户B)进行个性化设置。 (在此处阅读有关该项目的更多信息)。
1.发现和期望管理-很好地设置用户期望，以避免错误的期望。 (1. Discovery and expectation management — Set user expectations well to avoid false expectations.)
1.1用户应了解该工具可以做什么和不能做什么 (1.1 Users should be aware of what the tool can, and cannot do)
● Clear and focused personalized functionalities and affordances: Affordances are what the environment offers the individual (Gibson, 1966). Clearly state what Alice has to offer to the caretaker and senior adult. What makes Alice unique is that she is entirely customized and configured by the caretaker for a specific senior adult. The robot knows the user’s daily schedule, phone numbers of friends and family, their positive memories and subjects they like to talk about, and their favorite songs. The clear functionalities set user expectations and avoid false expectations.
● 明确且有针对性的个性化功能和赠品：“赠款是环境为个人提供的东西(Gibson，1966年)。 明确说明爱丽丝必须提供给看护人和成年成年人的东西。 爱丽丝之所以与众不同，是因为她完全由看管人定制并配置为特定的高级成年人。 机器人知道用户的日程安排，朋友和家人的电话号码，他们的积极记忆和他们喜欢谈论的主题以及他们喜欢的歌曲。 清晰的功能可设定用户期望值，并避免错误的期望值。
● Media material: By creating product manuals and videos, the functionalities and affordances are communicated to the user to set user expectations.
● 媒体材料：通过创建产品手册和视频，将功能和赠品传达给用户以设置用户期望。
● Onboarding with a trusted person to trigger copying behavior: The aim is for the senior adult to create an affective bond with the robot. According to professor Konijn of VU University (2016), a medium (like the robot) needs to be relevant to the user to create an affective bond. Thus, by having a familiar, trusted person showing the senior adult the personalized, relevant functionalities and affordances, the senior adult copies the behavior and knows what they can expect. Research indicates that senior adults start creating an affective bond with the robot after three sessions interacting with the robot. The longer the period of a session (>6 hours), the more the senior user enjoyed the presence of the robot.
● 与受信任的人一起入职以触发复制行为：旨在使老年人与机器人建立情感纽带。 根据VU大学的Konijn教授(2016)的说法，一种媒介(如机器人)需要与用户相关才能建立情感纽带。 因此，通过让熟悉的受信任的人向老年人显示个性化的相关功能和能力，老年人可以复制行为并知道他们可以期望什么。 研究表明，在与机器人互动三个环节之后，老年人开始与机器人建立情感纽带。 会话时间(> 6小时)越长，高级用户越会喜欢机器人的存在。
● Visual cues: We embedded an RGB light depicting listening (blinking red light), talking (flashing blue light), and processing (blinking yellow) so the user knows what is happening via visual cues.
● 视觉提示：我们嵌入了RGB灯，用于描述听(闪烁的红光)，讲话(闪烁的蓝光)和处理(闪烁的黄光)，以便用户通过视觉提示了解发生了什么。
1.2用户应该从最少的投入中受益 (1.2 Users should expect a benefit from minimal input)
● Connecting systems (robot, app, dashboard): Caretakers have a schedule for day-to-day tasks and activities for their clients. With the app, caretakers enter the schedule in the application. Caretakers are already used to creating schedules for their senior adults, and therefore this is ‘minimal input.’ The app connects to ‘Social robot Alice,’ and Alice handles the rest.
● 连接系统(机器人，应用程序，仪表板)：守卫者为客户制定了日常任务和活动的时间表。 使用该应用程序，看护人可以在应用程序中输入计划。 看护者已经习惯于为其高龄成年人制定时间表，因此这是“最少的投入”。 该应用程序连接到“社交机器人爱丽丝”，其余由爱丽丝处理。
● Feedback loop: Alice and the app connect to a dashboard that helps caretakers and designers to train Alice and can give recommendations and predictions on how to further personalize Alice for the user based on the activity log.
● 反馈循环：爱丽丝和应用程序连接到仪表板，该仪表板可帮助看护者和设计师培训爱丽丝，并可以根据活动日志为用户提供进一步的个性化建议和建议。
1.3为未发现的意外使用做好准备 (1.3 Prepare for undiscovered and unexpected usage)
● Survival pilot: In the last few months, we ran a survival pilot with Alice. Alice spent full days with the elderly for us to check whether she can survive a full day. During survival pilot tests, we observe the robot interacting with participants, and we discovered unexpected usage. These insights, in turn, were translated into user stories and requirements for the development team.
● 生存飞行员：在过去的几个月中，我们与爱丽丝一起进行了生存飞行员。 爱丽丝与老人一起度过了整整一天，以检查她是否可以存活一整天。 在生存飞行员测试期间，我们观察到机器人与参与者的互动，并且发现了意外的用途。 这些见解又转化为用户故事和对开发团队的要求。
● Motion sensor: During user tests, we found that the participants treat the robot as an actual entity (likely due to the affective bond). When they want the robot’s attention, they wave their hand in front of her eyes. Based on this discovery, we implemented a motion sensor near Alice’s eyes to recognize the senior adult’s waving behavior so that the robot can ‘turn on’ and give the user its attention.
● 运动传感器：在用户测试期间，我们发现参与者将机器人视为真实实体(可能是由于情感纽带)。 当他们想要机器人的注意力时，他们将手在她的眼前挥舞。 基于这一发现，我们在爱丽丝的眼睛附近安装了一个运动传感器，以识别老年人的挥手行为，从而使机器人可以“打开”并给予用户注意。
● Presence: Another unexpected finding is that the robot’s presence itself is already comforting enough and makes them feel less alone. The more lonely a user is, the more likely they are to accept the robot. We measured the degree of loneliness before and after user test sessions.
●临场感：另一个意外发现是机器人的临场感本身已经足够舒适，使他们感到不那么孤独。 用户越孤独，他们接受机器人的可能性就越大。 我们测量了用户测试会话前后的孤独程度。
1.4向用户宣传意外情况 (1.4 Educate the user about the unexpected)
● Repeating to create awareness: The robot often repeats that she is six years old. By repeating this, it manages user expectations that the robot is still young and might show unexpected behavior like children do (e.g., suddenly say or do things out of the ordinary).
● 重复以提高意识：机器人经常重复说自己已经六岁了。 通过重复此操作，它可以管理用户对机器人还很年轻的期望，并可能表现出像孩子一样的意想不到的行为(例如，突然说出或做些不寻常的事情)。
2.原谅的设计-AI会犯错误。 设计UI，以便用户倾向于原谅。 (2. Design for forgiveness — The AI will make mistakes. Design the UI, so users are inclined to forgive it.)
2.1设计工具时，用户会在出现错误时原谅它 (2.1 Design the tool in a way that users will forgive it when it makes mistakes)
● Personality/ character: A way to design for forgiveness is to use a UI that simulates creatures or objects that humans are already naturally inclined to forgive, like children or animals. Therefore we designed the robot to be a 6-year-old curious empathic child. She is happy, can show her 'sad face', is interested in her user, and loves to make her surroundings happy (view personality scales).
● 个性/性格：设计宽恕的一种方法是使用UI模拟人类已经自然倾向于宽恕的生物或物体，例如儿童或动物。 因此，我们将机器人设计为一个6岁的好奇同情孩子。 她很高兴，可以露出自己的“悲伤的面Kong”，对她的用户感兴趣，并且喜欢使周围的环境变得快乐( 查看个性量表 )。
● Exterior: The “uncanny valley”-phenomenon, first coined by Japanese roboticist Masahiro Mori (1970), refers to the idea that if a robot resembles a human too much, it can create feelings of disgust toward the robot (Lanteigne, 2019). We designed the current robot exterior together with more than 20 senior adults to create an exterior that’s appealing to the target group. The result is that the robot does not look very advanced (white, matt, simple). Interesting is how their final design does not depict a resemblance of real human likeness. However, there are a few human-like-characteristics like having eyes, eyebrows, and a physical head. This version of the robot fits between a non-human and humanoid (see image below). This would suggest that this exterior would increase the likelihood of a user’s empathy toward the robot.
● 外观： “不可思议的山谷”现象，最早由日本机器人家森正宏(Masahiro Mori，1970)提出，指的是如果机器人与人的相似度过高 ，就会对机器人产生厌恶感( Lanteigne ，2019 )。 我们与20多个高级成年人一起设计了当前的机器人外观，以创造一种吸引目标人群的外观。 结果是机器人看上去不太高级(白色，亚光，简单)。 有趣的是，它们的最终设计没有描绘出真实的人类相似之处。 但是，有一些类似人的特征，如眼睛，眉毛和身体的头部。 此版本的机器人适合非人类和类人动物(见下图)。 这表明该外观将增加用户对机器人的同情的可能性。
Also, we discovered that senior adults associate black with death, so we purposely do not embed any shiny futuristic black material. And, we offer a range of cuddly jackets (like teddy jackets, wool knit jackets, and hoodies) to make the robot more approachable and cuddly. An unexpected discovery is that women show interest in knitting clothes for the robot themselves. This shows that the woman presents signs of ‘anthropomorphism.’ This means senior adults tend to assign ‘human characteristics’ to the robot themselves.
此外，我们发现老年人将黑色与死亡联系在一起，因此我们故意不嵌入任何闪亮的未来派黑色材料。 并且，我们提供了一系列可爱的夹克(例如泰迪夹克，羊毛针织夹克和连帽衫)，以使机器人更加平易近人和可爱。 一个出乎意料的发现是，女性对为机器人自己编织衣服感兴趣。 这表明该妇女表现出“拟人化”迹象。 这意味着老年人倾向于给机器人自己分配“人性”。
● Visual cues: It was a discussion of how, and if we wanted to depict emotions with eyes. We experimented with lights, projections, and video screens. After testing, we chose to use .mp4 videos on a screen to communicate emotions through eye animations. This route allows us to iterate and update the design of the eyes easily and quickly. The eyes communicate the robot being unsure before answering questions (if the confidence level of understanding the user is low) to manage expectations.
● 视觉提示：这是关于如何以及是否要用眼睛描绘情感的讨论。 我们尝试了灯光，投影和视频屏幕。 经过测试后，我们选择在屏幕上使用.mp4视频，以通过眼睛动画传达情感。 此路线使我们可以轻松快速地迭代和更新眼睛的设计。 在回答问题(如果理解用户的置信度低)之前，眼睛会传达不确定的机器人来管理期望。
● Write dialog based on the user’s typical way of talking: We found that senior adults have a different way of speech due to experiencing and growing up in a different time. Heck, for all people, the way of conversing is different due to upbringing, culture, religion, language, social bonds. All these affect one’s typical way of talking. For us, we learned from the users’ way of speech and trained the robot to recognize intents and commands like how they spoke back in the day, so they feel more connected with the robot.
● 根据用户的典型讲话方式编写对话 ：我们发现老年人由于在不同的时间经历和成长而具有不同的讲话方式。 哎呀，由于所有人的成长，文化，宗教，语言，社会纽带，对于所有人来说，交谈的方式是不同的。 所有这些都会影响一个人的典型讲话方式。 对于我们来说，我们从用户的讲话方式学习和训练机器人识别意图和命令，就像他们在 白天怎样说话回来 ，让他们觉得与机器人更连接。
● Verbal language: The robot uses child-like ways of speaking, like using diminutives, short sentences, and telling stories in a child-like way. Also, it happens that the robot does not understand commands. For this, we created several fallback algorithms and sentences so the robot can repeat the most likely command and intent for the user to confirm (or not).
● 口头语言：机器人使用类似于儿童的说话方式，例如使用小量词，简短句子和以类似于儿童的方式讲故事。 另外，机器人可能无法理解命令。 为此，我们创建了多个后备算法和语句，以便机器人可以重复最有可能的命令和意图，以供用户确认(或不确认)。
ExampleUser: Alice, (*muttering*)Robot: (confidence level: 0,0*)Robot: {repeat possible intent=”Do you want me to play music?”) (guessing)User: Yes, I want you to play musicOrUser: No & rephrase+repeat questionIf(confidence level=<0*),then(repeat most likely intent as question)and(user:decision(yes,no),then(execute_or_fallback)*Intent matches have an intent detection confidence value in a range from 0.0 (completely uncertain) to 1.0 (completely certain).● Voice engineering: We changed the pitch of the Google voice to resemble a high-tone voice of a child. We changed the pace too. Users act more empathic when the robot has child-like features. We created an algorithm to change intonation, speed, breaks, and timing of words in sentences. We categorized script blocks and sentences and designed an algorithm that predicts which sentences need non-lexical sounds and which emotion to be outputted in the predicted appropriate way.
● 语音工程 ：我们更改了Google语音的音高，使其类似于孩子的高音。 我们也改变了脚步。 当机器人具有类似孩子的功能时，用户会表现出更多的同情心。 我们创建了一种算法来更改句子中单词的语调，速度，中断和时间安排。 我们对脚本块和句子进行了分类，并设计了一种算法，该算法可以预测哪些句子需要非词汇的声音，并以预测的适当方式输出哪种情感。
Example*User calls ticket agency and gets greeted by Voicebot*Voicebot: (introduction&) Would you like to book a flight, cancel, or change your booking?User: My cat just died. So, uuh… I would like to cancel”-- How to respond? --Voicebot: “O. (break=1sec) I’m sorry to hear that. (break=1sec)”Normally, there is always a standard break between sentences after a point. This little break could plant doubt and uncertainty and influence the user in a negative way.[listen to soundfile: unempathic response]Vs.Voicebot: “Oooohhhh-I’m-sorry-to-hear-that!.”(no breaks, gives immediate reassurance)[listen to soundfile: empathic response]Thanks to Phoebe and Maikel for the workshop they gave on voicebranding! voicebranding.nl● Natural language understanding response: You need to take into account the user and context. For example, in the first example, if one’s husband has just passed away, it’s not advisable to respond with trying to cheer them up with: ‘You-have-so-many-friends. It-will-be-alright!+[happy-eyes-emotion]’. Then it's better to respond in the following way: 'Aah-I-understand-you-are-sad. It-must-be-difficult. You-have-had-a-hard-life'+[sad eyes-emotion]. However, this is a difficult scenario and difficult to predict this immediately in the correct way. Therefore we are still analyzing, monitoring, and training Alice to have a high confidence level in her response. Also, it is crucial to take into account the amount of ‘negative sentiment’ over a period of time. If a user has been outputting happy sentiments and sentences for a month, but suddenly is uttering negative sentences, then Alice is programmed to respond happily still. However, if a senior user has been constantly very negative over a month, then the algorithm needs to use this data to respond accordingly. The system signals this negative speech behavior to the caretaker, and the caretaker can give this senior user more human attention.
● 自然语言理解响应：您需要考虑用户和上下文。 例如，在第一个示例中，如果丈夫刚刚去世，不建议用以下方法使他们振作起来：' 你有很多朋友。 会没事的！+ [happy-eyes-emotion] '。 然后最好以以下方式做出回应： “啊，我明白你很伤心。 它必须是困难的。 您过着艰苦的生活 + [悲伤的眼睛情绪] 。 但是，这是一个困难的情况，并且很难以正确的方式立即进行预测。 因此，我们仍在分析，监视和培训Alice，使其对自己的React具有很高的置信度。 同样，至关重要的是要考虑一段时间内的“负面情绪”。 如果用户已经输出了一个月的快乐情绪和句子，但是突然说出了否定的句子，则可以对Alice进行编程，使其仍然能够愉快地做出响应。 但是，如果一个高级用户在一个月内一直非常负面，则该算法需要使用此数据做出相应的响应。 系统将这种消极的言语举止发送给看护者，看护者可以给这个高级用户更多的注意力。
ExampleHow to analyze and predict whether Alice needs to respond happy
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