Vowel harmony and positional variation in Kyrgyz

Adam G. McCollum

doi:10.5334/labphon.247

Nature of Contrast and Coarticulation: Evidence from Mizo Tones and Assamese Vowel Harmony

10.21437/interspeech.2017-1304 ◽

2017 ◽

Author(s):

Indranil Dutta ◽

Irfan S. ◽

Pamir Gogoi ◽

Priyankoo Sarmah

Keyword(s):

Vowel Harmony

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Summary and areas for further research

10.1093/oso/9780198813576.003.0011 ◽

2018 ◽

Author(s):

Harry van der Hulst

Keyword(s):

Special Section ◽

Vowel Harmony ◽

Simple Idea

This chapter summarizes the theory and the typology of the vowel harmony systems it predicts. The summary recaptures the simple idea that formed the beginning of the theory (including the use of variable elements and licensing) and then summarizes how the use of variable elements was extended to include both vowels that alternate harmonically and neutral vowels. A special section is devoted to the notion of system dependency which discusses cases of parasitic harmony, including approaches that extend this notion beyond the cases that have been analyzed in this book. The chapter concludes with suggestions for further research.

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Aperture harmony

10.1093/oso/9780198813576.003.0006 ◽

2018 ◽

Author(s):

Harry van der Hulst

Keyword(s):

Vowel Harmony ◽

Romance Languages ◽

Common Element ◽

Binary Features ◽

High Vowels

This chapter analyzes a number of vowel harmony systems which have been described or analyzed in terms of aperture (lowering or raising, including complete harmony). This takes us into areas where the literature on vowel harmony discusses cases involving the following binary features: [± high], [± low], [± ATR], and [± RTR]. Raising has been thought of as problematic for unary ‘IUA’ systems as these systems lack a common element for high vowels. This chapter suggests that raising can be attributed to ATR-harmony. The chapter also discusses typological generalizations and analyzes metaphony in Romance languages.

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Harmony as licensing

10.1093/oso/9780198813576.003.0003 ◽

2018 ◽

Author(s):

Harry van der Hulst

Keyword(s):

Vowel Harmony ◽

Selection Mechanism ◽

Phonological Structure ◽

Explicit Theory ◽

Selection Of

This chapter develops an explicit theory of vowel harmony based on unary elements and lateral and positional licensing which is embedded in a general dependency-based theory of phonological structure (called ‘Radical CV Phonology’). Harmony is analyzed in terms of a licensing requirement, which results in ‘agreement’, both intra-morphemically and inter-morphemically, that is, within the domain of the word In essence, the view put forward is that lexical vowel harmony involves the selection of lexically listed allomorphs. Licensing will be the selection mechanism for the proper allomorph. The chapter discusses the treatment of morpheme-internal harmony, trigger and targets in harmony, and the notion of cyclicity.

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Assamese vowels and vowel harmony

Journal of South Asian Languages and Linguistics ◽

10.1515/jsall-2019-2010 ◽

2020 ◽

Vol 6 (2) ◽

pp. 151-183

Author(s):

Diana B. Archangeli ◽

Jonathan Yip

Keyword(s):

Physical Properties ◽

Principal Components Analysis ◽

Principal Components ◽

Vowel Harmony ◽

Language Community ◽

Acoustic Data ◽

Apparent Loss ◽

Root Position ◽

Components Analysis ◽

Open Question

AbstractBased on impressionistic and acoustic data, Assamese is described as having a phonological tongue root harmony system, with blocking by certain phonological configurations and over-application in certain morphological contexts. This study explores physical properties of the patterns using ultrasonic imaging to determine whether the impressionistic descriptions match what speakers actually do. Principal components analysis (PCA) determines that most participants produce a contrast in tongue root position in the appropriate contexts, though there is less of an impact on tongue root with greater distance from the triggering vowel. Analysis uses the root mean squared distance (RMSD) calculation to determine whether both blocking and over-application take effect. The blocking results conform to the impressionistic descriptions. With over-application, [e] and [o] are expected; while some speakers clearly produce these vowels, others articulate a vowel that is indeterminant between the expected [e]/[o] and an unexpected [ɛ]/[ɔ]. No speaker consistently showed the expected tongue root position in all contexts, and some speakers appeared to have lost the contrast entirely, yet all are considered to be speakers of the same dialect of Assamese. Whether this (apparent) loss is a consequence of crude research methodologies or accurately reflects what is happening within the language community remains an open question.

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Stochastic phonological knowledge: the case of Hungarian vowel harmony

Phonology ◽

10.1017/s0952675706000765 ◽

2006 ◽

Vol 23 (1) ◽

pp. 59-104 ◽

Cited By ~ 90

Author(s):

Bruce Hayes ◽

Zsuzsa Cziráky Londe

Keyword(s):

Web Search ◽

Native Speakers ◽

Vowel Harmony ◽

Generation Model ◽

General Process ◽

Phonological Knowledge ◽

Statistical Variation ◽

Take Back ◽

Back Vowel ◽

Dual Listing

In Hungarian, stems ending in a back vowel plus one or more neutral vowels show unusual behaviour: for such stems, the otherwise general process of vowel harmony is lexically idiosyncratic. Particular stems can take front suffixes, take back suffixes or vacillate. Yet at a statistical level, the patterning among these stems is lawful: in the aggregate, they obey principles that relate the propensity to take back or front harmony to the height of the rightmost vowel and to the number of neutral vowels. We argue that this patterned statistical variation in the Hungarian lexicon is internalised by native speakers. Our evidence is that they replicate the pattern when they are asked to apply harmony to novel stems in a ‘wug’ test (Berko 1958). Our test results match quantitative data about the Hungarian lexicon, gathered with an automated Web search. We model the speakers' knowledge and intuitions with a grammar based on the dual listing/generation model of Zuraw (2000), then show how the constraint rankings of this grammar can be learned by algorithm.

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