Data-driven articulatory modelling: foundations for a new generation of speech synthesis

在过去的五年里，自动生成人工语音(语音合成)的技术已经变得足够自然，以至于它的使用已经戏剧性地扩大。业界的领先者已将文本到语音(TTS)系统集成到有用的实际应用中，例如自动呼叫中心和呼叫路由、基于电话的信息系统(例如电话银行或新闻服务)、为视障人士提供的阅读器，以及免提界面(例如汽车导航系统)。然而，尽管取得了这些成功，最先进的TTS系统在控制方面仍然受到严重限制。简而言之，我们可以很容易地控制合成器说什么，但不能控制他们说什么。因此，虽然这样的系统适合于以语音形式提供事实信息，但在需要高水平的表现力时，它们完全不够。我们所说的表现力是指在选定的单词上表示问题或强调的能力，或者传达情感的能力。此外，产生新的合成声音的过程是昂贵和劳动密集型的。该项目的目的是开发一种替代当前语音合成技术的方法，该技术具有相当的可理解性和自然度，但提供了更大的灵活性和可控性。单元选择使用大量预先录制的语音集合，仅通过按顺序将适当的片段粘合在一起来执行合成。实际上，几乎没有或根本没有涉及到语音建模。相比之下，这个项目的目标是开发一种新的模型，该模型以预先录制的语音为基础进行训练，并以一种新的方式解释它：基于其潜在的发音。该模型的目的是生成合成语音，它不仅保留了用于训练的原始语音的质量，而且更具多功能性，因此具有在新的和令人兴奋的方式中使用的潜力。

Technology to automatically generate artificial speech (speech synthesis) has come to sound natural enough within the past five years that its use has widened dramatically. Leaders in industry have integrated text-to-speech (TTS) systems into useful real-world applications, such as automated call-centres and call routing, telephone-based information systems (e.g. telephone banking or news services), readers for the visually impaired, and hands-free interfaces, such as car navigation systems.However, in spite of this success, state-of-the-art TTS systems are still severely limited in terms of control. In short, we can readily control what synthesisers say, but not how they say it. Therefore, although such systems are suitable for giving factual information in speech form, they are completely inadequate where a high level of expressiveness is required. By expressiveness we mean the ability to indicate questions or emphasis on selected words, or to convey emotion. Furthermore, the process of generating new synthetic voices is costly and labour-intensive. It is the aim of this project to develop an alternative to current speech synthesis technology with a comparable level of intelligibility and naturalness, but which affords far greater flexibility and control.Unit selection uses large collections of pre-recorded speech to perform synthesis by merely gluing together appropriate fragments in sequence. There is in effect little or no modelling of speech involved. In contrast, this project aims to develop a new model which is trained on pre-recorded speech and interprets it in a novel way: on the basis of its underlying articulation. The aim of this model is to produce synthetic speech which not only retains the qualities of the original speech used for training, but which also is much more versatile and therefore has the potential to be used in new and exciting ways.