SyPhon: A Framework for Automated Phonological Reasoning

SyPhon：自动语音推理框架

• 批准号: 2021149
• 负责人: Eric Bakovic
• 金额: $ 40.26万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021149&HistoricalAwards=false
• 关键词: SyPhon; Framework; Automated; Phonological; Reasoning

This project develops new tools for the study of phonology, the sound patterns in human languages. For example, phonology is concerned with explaining why the past tense suffix of different English verbs is pronounced differently: “begg ed” is pronounced [beg d], while “zipp ed” is pronounced [zip t]. The explanation in this case is a phonological process that turns the past tense suffix /d/ into its voiceless counterpart [t] in [zip t] because it occurs after a voiceless consonant /p/. Phonological inference is the problem of discovering a formal description of a phonological process that explains given data (e.g. examples of English verb form pronunciations). Inference is an error-prone and time-consuming task for a phonologist, especially given that inference results depend on the formalism used to describe processes, and there is no single formalism universally accepted in the community. On the contrary, phonologists continuously propose new and refine existing formalisms in order to explain more and more observed language data.The goal of this project is to build a software framework, SyPhon, that automates the process of phonological inference. SyPhon takes as input datasets that illustrate phonological processes, as well as a specification of the formal language for describing processes. The framework produces as output the optimal explanation (according to some cost function) of the given data in a given formal language. SyPhon enables phonologists to rapidly explore different theories, by varying the formal language and the cost function, and observing the inference results on a dataset. The core technical challenge of this project is the extreme computational cost of phonological inference, which requires searching a large space of possible formal descriptions. To make such inference feasible, the investigators leverage state-of-the-art techniques from an area of computer science called program synthesis; these techniques allow SyPhon to reduce the search problem to a constrained optimization problem that is efficiently solvable in practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目为语音学的研究开发了新的工具，人类语言中的声音模式。例如，音系学关注的是解释为什么不同英语动词的过去式后缀发音不同：“begg艾德”发[beg d]，而“zipp艾德”发[zip t]。这种情况下的解释是一个语音过程，它将过去时态后缀/d/变成[zip t]中的清辅音[t]，因为它出现在清辅音/p/之后。语音推理是发现一个语音过程的正式描述，解释给定的数据（例如英语动词形式发音的例子）的问题。对于音系学家来说，推理是一项容易出错且耗时的任务，特别是考虑到推理结果取决于用于描述过程的形式主义，并且在社区中没有普遍接受的单一形式主义。相反，语音学家不断提出新的和完善现有的形式主义，以解释越来越多的观察到的语言data.The项目的目标是建立一个软件框架，SyPhon，自动化的语音推理过程。SyPhon将说明语音过程的数据集以及用于描述过程的形式语言的规范作为输入。该框架以给定的形式语言产生给定数据的最佳解释（根据某些成本函数）作为输出。SyPhon使语音学家能够通过改变形式语言和成本函数，并观察数据集上的推理结果，快速探索不同的理论。该项目的核心技术挑战是语音推理的极端计算成本，这需要搜索大量可能的形式描述。为了使这种推理变得可行，研究人员利用了计算机科学领域称为程序合成的最先进技术;这些技术使SyPhon能够将搜索问题简化为在实践中可以有效解决的约束优化问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。