TarDiS: Targets and Dynamics in Speech

言语是一种物理现象：它可以表现为一系列声道运动，也可以表现为声波。然而，为了使言语发挥交际功能，它还必须包括更多的抽象成分，这些成分是将这些物理现象与语言意义配对的基石。例如，单词“kite”中的元音/ai/可以用多种方式表示。在生理上，它涉及舌头从较低和较远的位置移动到嘴角的高前角，但同时/ai/是一个抽象的范畴，是一个由它们共同的有意义的功能聚集在一起的声音家族。元音/ai/是英语中的一个对比单位，它使“kite”这样的单词与“kit”这样的单词不同。大多数以英语为母语的人从来不需要思考范畴/ai/的性质，但他们不知何故获得了一个系统的映射，将/ai/的物理形式与其含义联系起来。对语言学家来说，理解这个过程是理解语言如何工作的关键。正是通过这种映射，舌头的运动获得了语言意义。本研究项目旨在探索元音类别（如/ai/）的性质，更具体地说，它们的抽象表示必须包含什么，以便成功地模拟语音中观察到的变化。这是语音科学中的一个老问题，我们打算使用一种新型的数据来解决这个问题：结合计算建模，基于在磁场中跟踪这种运动的舌头运动测量，以及用超声采集的舌头图像，以及音频信号。我们将记录来自一个单一的语言社区（西约克郡）的20个母语者在他们的各种英语中发出所有元音的数据。然后，我们将考虑我们可以从物理信号中获得的各种抽象特征，以及成功捕获所有元音类别所需的最小数量的此类特征。例如，我们想知道像/ai/这样的元音是否最好由一个或两个元素组成，以及为了成功地在元音系统中建模/ai/，我们需要编码多少关于运动的信息。虽然元音类别和元音目标在语言的自然习得中并不明显，但在显性语音教学发挥作用的情况下，如语音治疗，它们变得很重要。我们对语音的本质了解得越多，我们在教授语音方面就越成功。因此，我们将从理论建模的角度向临床语音研究人员和临床实践者展示我们对语音目标的研究结果，并让他们参与讨论如何将这些抽象的语音目标用作语音治疗的工具。

Speech is a physical phenomenon: it can be represented as a series of vocal tract movements, or as sound waves. However, in order for speech to serve a communicative function, it also must include more abstract components, building blocks that pair these physical phenomena with linguistic meaning. For instance, the vowel /ai/ in a word like 'kite', can be represented in multiple ways. Physiologically, it involves tongue movement from a lower and more far back position to the high front corner of the mouth, but at the same time /ai/ is an abstract category, a family of sounds brought together by their joint meaningful function. The vowel /ai/ is a contrastive unit in English, one that makes words like 'kite' different from words like 'kit'. Most native speakers of English never need to ponder the nature of the category /ai/, yet they somehow acquire a systematic mapping that links the physical form of /ai/ with its meaning. To linguists, understanding this process holds the key to understanding how language works. It is by this type of mapping that a movement of the tongue acquires linguistic meaning. This research project sets out to probe the nature of vowel categories like /ai/, and more specifically what their abstract representations must contain in order to successfully model variation observed in speech. This is an old problem in speech science, which we intend to approach using a novel type of data: combination of computational modelling, measurements of tongue movement based on tracking such movement in a magnetic field, and images of tongue acquired with ultrasound, as well as the audio signal. We will record such data from twenty native speakers from a single speech community (West Yorkshire) pronouncing all vowels in their variety of English. We will then consider various abstract features that we can derive from the physical signal, and the minimum number of such features needed to successfully capture all vowel categories. For instance, we want to know whether vowels such as /ai/ are best modelled as consisting of one or two elements, and how much information about movement we need to encode in order to successfully model /ai/ within a vowel system.We will extend the implications of our findings to practical applications. Although vowel categories and vowel targets do not feature overtly in natural acquisition of language, they become important in situations where explicit speech instruction plays a role, such as speech therapy. The more we understand about the nature of speech sounds, the more successful we can be in teaching them. Therefore, we will present our findings on speech goals from a theoretical modelling perspective to clinical speech researchers and clinical practitioners, and engage them in a discussion of how such abstract speech targets can be used as tools in speech therapy.