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Grammaticus ex machina : tone inventories as hypothesized by machine Fry, Michael David

Abstract

A fundamental task of linguistics is to accurately describe the sound patterns of a language. In the field of phonology, this often starts with identifying the set of contrastive sounds in the language, its phoneme inventory. If the language under investigation is a tone language, then identifying the contrastive tones in the language, its tone inventory, is also needed. Historically, phonologists have identified phoneme and tone inventories through lengthy elicitation sessions in order to determine contrasting units. Yet, given the recent advances in machine learning, there may be another way. In this thesis, I argue, by way of demonstration, that machine learning has become a valuable tool for field and theoretical linguists in the description of language and in the development of linguistic theory. Specifically, I present empirical support, using machine learning methods, for the theory of Emergent Phonology, which holds that phonology emerges as the "consequence of accumulated phonetic experience'' (Lindblom, 1999, p. 195). This support comes in the form of hypothesized tone inventories (part of one's phonology) that emerge, via an unsupervised learning model, from acoustic-phonetic data for a given language. Since the hypothesized inventories match fairly well with the tone inventories standardly reported in the literature, an aspect of phonology is shown to have emerged from phonetics and support for Emergent Phonology is achieved. To test the robustness of the unsupervised learning method, it is applied to four languages: Mandarin, Cantonese, Fungwa and English. Finally, since the identification of tone inventories has hitherto been under the purview of human linguists, success in this project provides a first step towards creating a grammaticus ex machina -- a linguist (grammarian) from the machine.

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