Morgan Sonderegger


2024

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Correlation Does Not Imply Compensation: Complexity and Irregularity in the Lexicon
Amanda Doucette | Ryan Cotterell | Morgan Sonderegger | Timothy J. O’Donnell
Proceedings of the Society for Computation in Linguistics 2024

2022

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Multidimensional acoustic variation in vowels across English dialects
James Tanner | Morgan Sonderegger | Jane Stuart-Smith
Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Vowels are typically characterized in terms of their static position in formant space, though vowels have also been long-known to undergo dynamic formant change over their timecourse. Recent studies have demonstrated that this change is highly informative for distinguishing vowels within a system, as well as providing additional resolution in characterizing differences between dialects. It remains unclear, however, how both static and dynamic representations capture the main dimensions of vowel variation across a large number of dialects. This study examines the role of static, dynamic, and duration information for 5 vowels across 21 British and North American English dialects, and observes that vowels exhibit highly structured variation across dialects, with dialects displaying similar patterns within a given vowel, broadly corresponding to a spectrum between traditional ‘monophthong’ and ‘diphthong’ characterizations. These findings highlight the importance of dynamic and duration information in capturing how vowels can systematically vary across a large number of dialects, and provide the first large-scale description of formant dynamics across many dialects of a single language.

2013

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Gender Inference of Twitter Users in Non-English Contexts
Morgane Ciot | Morgan Sonderegger | Derek Ruths
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2010

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Combining Data and Mathematical Models of Language Change
Morgan Sonderegger | Partha Niyogi
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics