Post-Nasal Devoicing and a Probabilistic Model of Phonological Typology
Gasper Begus
November 2018 

This paper addresses one of the most contested issues in phonology: unnatural alternations. First, non-natural phonological processes are subdivided into unmotivated and unnatural. The central topic of the paper is an unnatural process: post-nasal devoicing. I collect thirteen cases of post-nasal devoicing and argue that in all reported cases, post-nasal devoicing does not derive from a single unnatural sound change (as claimed in some individual accounts of the data), but rather from a combination of three sound changes, each of which is phonetically motivated. I present new evidence showing that the three stages are directly historically attested in the pre-history of Yaghnobi. Based on several discussed cases, I propose a new diachronic model for explaining unnatural phenomena called the Blurring Process and point to its advantages over competing approaches (hypercorrection, perceptual enhancement, and phonetic motivation). The Blurring Process establishes general diachronic conditions for unnatural synchronic processes and can be employed to explain unnatural processes beyond post-nasal devoicing. Additionally, I provide a proof establishing the minimal sound changes required for an unmotivated/unnatural alternation to arise. The Blurring Process and Minimal Sound Change Requirement have implications for models of typology within the Channel Bias approach. This paper thus presents a first step towards the ultimate goal of quantifying the influences of Channel Bias on phonological typology.

Format:	[ pdf ]
Reference:	lingbuzz/003232 (please use that when you cite this article)
Published in:	To appear in Journal of Linguistics. Note the change of the title. The second part of the paper has been extended to a separate manuscript: https://ling.auf.net/lingbuzz/004299
keywords:	phonological typology, probabilistic modeling, bootstrapping, sound change, naturalness, channel bias, voice, post-nasal devoicing
previous versions:	v2 [September 2017] v1 [December 2016]
Downloaded:	1215 times

 

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