Computational Phonology

by

Jane Chandlee

• LAST REVIEWED: 25 August 2022
• LAST MODIFIED: 30 October 2019
• DOI: 10.1093/obo/9780199772810-0249

Introduction

Much like the term “computational linguistics”, the term “computational phonology” has come to mean different things to different people. Research grounded in a variety of methodologies and formalisms can be included in its scope. The common thread of the research that falls under this umbrella term is the use of computational methods to investigate questions of interest in phonology, primarily how to delimit the set of possible phonological patterns from the larger set of “logically possible” patterns and how those patterns are learned. Computational phonology arguably began with the foundational result that Sound Pattern of English (SPE) rules are regular relations (provided they can’t recursively apply to their own structural change), which means they can be modeled with finite-state transducers (FSTs) and that a system of ordered rules can be composed into a single FST. The significance of this result can be seen in the prominence of finite-state models both in theoretical phonology research and in more applied areas like natural language processing and human language technology. The shift in the field of phonology from rule-based grammars to constraint-based frameworks like Optimality Theory (OT) initially sparked interest in the question of how to model OT with FSTs and thereby preserve the noted restriction of phonology to the complexity level of regular. But an additional point of interest for computational work on OT stemmed from the ways in which its architecture readily lends itself to the development of learning algorithms and models, including statistical approaches that address recognized challenges such as gradient acceptability, process optionality, and the learning of underlying forms and hidden structure. Another line of research has taken on the question of to what extent phonology is not just regular, but subregular, meaning describable with proper subclasses of the regular languages and relations. The advantages of subregular modeling of phonological phenomena are argued to be stronger typological explanations, in that the computational properties that establish the subclasses as properly subregular restrict the kinds of phenomena that can be described in desirable ways. Also, these same restrictions lead directly to provably correct learning algorithms. Once again this work has made extensive use of the finite-state formalism, but it has also employed logical characterizations that more readily extend from strings to non-linear phenomena such as autosegmental representations and syllable structure.

General Overviews

Overviews of computational phonology necessarily acknowledge the range of research methods it encompasses, as Daland 2014 notes in a comparison of the most prominent methods being pursued today. Chandlee and Heinz 2017 emphasizes the framing of phonological research questions as computational problems, while Heinz 2011a and Heinz 2011b provide a welcoming introduction to the mathematical foundations of such problems. In an introduction to a special computational issue of the journal Phonology, Heinz and Idsardi 2017 summarizes the key themes arising from the diverse sample of papers included in this issue. And in a chapter of a popular textbook, Jurafsky and Martin 2009 describes finite-state implementations of prominent phonological theories and gives a survey of learning results. Hulden 2018 also introduces finite-state technology in the context of its extensive use in computational phonology and morphology.

• Chandlee, Jane, and Jeffrey Heinz. 2017. Computational phonology. In Oxford research encyclopedia of linguistics. Edited by Mark Aronoff. Oxford: Oxford Univ. Press.

  Characterizes the questions of interest to phonologists as computational problems and summarizes key results in a variety of approaches.

• Daland, Robert. 2014. What is computational phonology? Loquens 1.1: e004.

  DOI: 10.3989/loquens.2014.004

  Identifies the various ways that computational phonology has been defined and comments on the relative merits of each approach.

• Heinz, Jeffrey. 2011a. Computational phonology part I: Foundations. Language and Linguistics Compass 5.4: 140–152.

  DOI: 10.1111/j.1749-818X.2011.00269.x

  Accessible introduction to the formal language theoretic approach to computational phonology.

• Heinz, Jeffrey. 2011b. Computational phonology part II: Grammars, learning, and the future. Language and Linguistics Compass 5.4: 153–168.

  DOI: 10.1111/j.1749-818X.2011.00268.x

  Continuation of Part 1 that surveys what is known about the computational properties of phonological theories and identifies the subregular hierarchy of formal languages as a fruitful approach to studying phonological patterns.

• Heinz, Jeffrey, and William Idsardi. 2017. Computational phonology today. Phonology 34.2: 211–219.

  DOI: 10.1017/S0952675717000112

  Introduces the special issue of Phonology on computational phonology, edited by Heinz and Idsardi. The issue itself includes a representative sample of papers on statistical techniques for model comparison, phonological learning simulations using Maximum Entropy Grammars, and formal language theoretic analyses of phonological patterns.

• Hulden, Mans. 2018. Finite-state technology. In The Oxford handbook of computational linguistics. 2d ed. Edited by Ruslan Mitkov. Oxford: Oxford Univ. Press.

  DOI: 10.1093/oxfordhb/9780199573691.013.39

  Introduction to the definitions and construction methods for finite-state automata and transducers, a formalism that pervades research in computational phonology and computational linguistics more generally.

• Jurafsky, Daniel, and James H. Martin. 2009. Computational phonology. In Speech and language processing. By Daniel Jurafsky and James H. Martin, 361–384. Upper Saddle River, NJ: Pearson Prentice Hall.

  Brief description of finite-state implementations of Sound Pattern of English (SPE) and Optimality Theory (OT) and a summary of key learning results for both theories.