ACE-LP: Augmenting Communication using Environmental Data to drive Language Prediction.

交流是生活的本质。我们通过多种方式进行交流，但正是我们的说话能力使我们能够在日常生活中聊天。据估计，仅在英国就有25万人不会说话，面临被孤立的风险。他们依靠语音输出通信辅助设备（VOCAs）来弥补他们的残疾。然而，目前最先进的voca只能以每分钟8到10个单词的速度产生计算机化语音。对于一些无法使用键盘的用户来说，速度甚至更慢。例如，斯蒂芬·霍金教授最近在他的VOCA软件中加入了一个更有效的单词预测系统和常用的快捷方式，将他的口语交流速度提高了一倍，达到每分钟2个字。尽管三十年来VOCAs的发展，面对面的交流速度仍然非常缓慢。用户很少超出基于基本需求的话语，因为速度最多比自然语言慢10倍。与典型语音的平均150-190 wpm相比，辅助通信速率使对话几乎不可能进行。ACE-LP汇集了增强和替代通信（AAC）（邓迪大学），智能交互系统（剑桥大学）和计算机视觉和图像处理（邓迪大学）的研究专业知识，开发了一个预测性的AAC系统，通过引入多模态传感器数据的使用来通知最先进的语言预测，该系统将解决这些令人难以置信的缓慢通信速率。这是VOCA系统第一次不仅能预测单词和短语；我们的目标是通过预测适合正在进行的对话的叙事文本元素来提供对扩展对话的访问。在目前的系统中，用户有时会在存储前进行独白“谈话”，但很少使用挥发性有机化合物互动地分享个人经验（故事）。能够将经验联系起来使我们能够与他人交往，并使我们能够参与社会。事实上，我们与他人的大部分互动都是通过对话叙事的媒介，即分享个人故事。几个研究项目已经建立了自动收集数据和语言处理的方法原型，这些方法可以帮助残疾用户更方便、更高效地进行交流。然而，还没有人成功地利用这种技术的潜力来设计一种集成通信系统，该系统可以自动从不同来源提取有意义的数据，将其转换为会话文本元素，并以这样一种方式呈现结果，即有严重身体残疾的人可以通过语音合成器快速地操作和选择会话项目，并以最小的身体和认知努力输出。该项目将开发一种技术，利用上下文数据（例如关于位置、对话伙伴和过去对话的信息）来支持屏幕用户界面中的语言预测，该界面将根据对话主题、对话伙伴、对话设置和不说话的人的身体能力进行调整。我们的目标是改善不会说话的人的交流体验，使他们能够以更容易接受的速度轻松地讲述自己的故事。

Communication is the essence of life. We communicate in many ways, but it is our ability to speak which enables us to chat in every-day situations. An estimated quarter of a million people in the UK alone are unable to speak and are at risk of isolation. They depend on Voice Output Communication Aids (VOCAs) to compensate for their disability. However, the current state of the art VOCAs are only able to produce computerised speech at an insufficient rate of 8 to 10 words per minute (wpm). For some users who are unable to use a keyboard, rates are even slower. For example, Professor Stephen Hawking recently doubled his spoken communication rate to 2 wpm by incorporating a more efficient word prediction system and common shortcuts into his VOCA software. Despite three decades of developing VOCAs, face-to-face communication rates remain prohibitively slow. Users seldom go beyond basic needs based utterances as rates remain, at best, 10 times slower than natural speech. Compared to the average of 150-190 wpm for typical speech, aided communication rates make conversation almost impossible.ACE-LP brings together research expertise in Augmentative and Alternative Communication (AAC) (University of Dundee), Intelligent Interactive Systems (University of Cambridge), and Computer Vision and Image Processing (University of Dundee) to develop a predictive AAC system that will address these prohibitively slow communication rates by introducing the use of multimodal sensor data to inform state of the art language prediction. For the first time a VOCA system will not only predict words and phrases; we aim to provide access to extended conversation by predicting narrative text elements tailored to an ongoing conversation.In current systems users sometimes pre-store monologue 'talks', but sharing personal experiences (stories) interactively using VOCAs is rare. Being able to relate experience enables us to engage with others and allows us to participate in society. In fact, the bulk of our interaction with others is through the medium of conversational narrative, i.e. sharing personal stories. Several research projects have prototyped ways in which automatically gathered data and language processing can support disabled users to communicate easily and at higher rates. However, none have succeeded in harnessing the potential of such technology to design an integrated communication system which automatically extracts meaningful data from different sources, transforms this into conversational text elements and presents results in such a way that people with severe physical disabilities can manipulate and select conversational items for output through a speech synthesiser quickly and with minimal physical and cognitive effort. This project will develop technology which will leverage contextual data (e.g. information about location, conversational partners and past conversations) to support language prediction within an onscreen user interface which will adapt depending on the conversational topic, the conversational partner, the conversational setting and the physical ability of the nonspeaking person. Our aim is to improve the communication experience of nonspeaking people by enabling them to tell their stories easily, at more acceptable speeds.