SyPhon: A Framework for Automated Phonological Reasoning

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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.