SCRIPT: Speech Synthesis for Spoken Content Production

跨多种语言制作动态更新的媒体内容（如在线视频新闻包）的成本非常高。维持每个语种的大量记者团队既昂贵又缺乏灵活性。英国广播公司（BBC）或英国《金融时报》（Financial Times）等现代媒体机构需要一种更灵活的方法：它们必须能够对不断变化的世界事件（例如突发新闻或新兴市场）做出快速反应，根据外部需求动态分配有限的资源。理想情况下，他们希望用以前不支持的语言创建“弹出式”服务和产品，然后在以后扩展或缩小它们。英国政府为BBC设定的目标是，到2022年，BBC的全球观众人数将达到5亿，而目前BBC的全球观众人数为3.08亿。要达到如此庞大的观众群，唯一的办法就是通过新的语言服务和高效的制作技术。文本到语音——从文本自动生成语音——为这一挑战提供了一个有吸引力的解决方案，BBC已经确定了计算机辅助翻译和文本到语音作为关键技术，将为他们提供跨多种语言创建和转换内容的新方法。该项目的目标是推动文本到语音的技术向“广播质量”计算机生成的语音（即，足以让BBC广播）的多种语言发展，并使其在以后添加更多语言时变得便宜和容易。我们将通过结合和扩展我们之前关于文本到语音的几个不同的基础研究来做到这一点。我们将使用最新的数据驱动机器学习技术，并扩展它们以产生更高质量的输出语音。与此同时，我们将使人类控制语言成为可能。这将允许使用者（例如，BBC记者）调整演讲，以确保质量和说话风格符合他们的目的（例如，纠正一个难的单词的发音，或者把重点放在正确的地方）。我们将为英国广播公司（BBC）等机构开发的技术，也将使规模较小的公司和其他组织、国家机构、慈善机构和个人能够快速创建高质量的口语内容，无论他们使用何种语言或领域。在项目期间，我们将与其他类型的组织合作，以确保我们创造的技术具有广泛的吸引力，并将对广泛的公司和个人有用。

The cost of producing dynamically-updated media content - such as online video news packages - across multiple languages is very high. Maintaining substantial teams of journalists per language is expensive and inflexible. Modern media organisations like the BBC or the Financial Times need a more agile approach: they must be able to react quickly to changing world events (e.g., breaking news or emerging markets), dynamically allocating their limited resources in response to external demands. Ideally, they would like to create `pop-up' services & products in previously-unsupported languages, then to scale them up or down later.The government has set the BBC a target of reaching a global audience of 500 million people by 2022, compared with today's 308 million. The only way to reach such a huge audience is through new language services and efficient production techniques. Text-to-speech - which automatically produces speech from text - offers an attractive solution to this challenge, and the BBC have identified computer assisted translation and text-to-speech as key technologies that will provide them with new ways of creating and reversioning their content across many languages.This project's objectives are to push text-to-speech technology towards "broadcast quality" computer-generated speech (i.e., good enough for the BBC to broadcast) in many languages, and to make it cheap and easy to add more languages later. We will do this by combining and extending several distinct pieces of our previous basic research on text-to-speech. We will use the latest data-driven machine learning techniques, and extend them to produce much higher quality output speech. At the same time, we will enable the possibility of human control over the speech. This will allow the user (e.g., a BBC journalist) to adjust the speech to make sure the quality and the speaking style is right for their purposes (e.g., correcting the pronunciation of a difficult word, or putting emphasis in the right place).The technology we will create for the likes of the BBC will also enable smaller companies and other organisations, state bodies, charities, and individuals to rapidly create high-quality spoken content, in whatever language or domain they are operating. We will work with other types of organisation during the project, to make sure that the technology we create has broad appeal and will be useful to a wide range of companies and individuals.