Probabilistic Typology: Deep Generative Models of Vowel Inventories

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1182–1192 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1109 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1182–1192 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1109 Probabilistic Typology: Deep Generative Models of Vowel Inventories Ryan Cotterell and Jason Eisner Department of Computer Science Johns Hopkins University {ryan.cotterell,eisner}@jhu.edu Abstract Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For ex- ample, all languages have vowels, while most—but not all—languages have an [u] sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a se- ries of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages. 1 Introduction Human languages exhibit a wide range of phenom- ena, within some limits. However, some structures seem to occur or co-occur more frequently than oth- ers. Linguistic typology attempts to describe the range of natural variation and seeks to organize and quantify linguistic universals, such as patterns of co-occurrence. Perhaps one of the simplest typolog- ical questions comes from phonology: which vow- els tend to occur and co-occur within the phoneme inventories of different languages? Drawing in- spiration from the linguistic literature, we propose models of the probability distribution from which the attested vowel inventories have been drawn. It is a typological universal that every language contains both vowels and consonants (Velupillai, 2012). But which vowels a language contains is guided by softer constraints, in that certain configurations are more widely attested than oth- ers. For instance, in a typical phoneme inven- tory, there tend to be far fewer vowels than con- sonants. Likewise, all languages contrast vowels based on height, although which contrast is made is language-dependent (Ladefoged and Maddieson, 1996). Moreover, while over 600 unique vowel Figure 1: The transformed vowel space that is constructed within one of our deep generative models (see §7.1). A deep network nonlinearly maps the blue grid (“formant space”) to the red grid (“metric space”), with individual vowels mapped from blue to red position as shown. Vowel pairs such as [@]– [O] that are brought close together are anti-correlated in the point process. Other pairs such as [y]–[1] are driven apart. For purposes of the visualization, we have transformed the red coordinate system to place red vowels near their blue positions—while preserving distances up to a constant factor (a “Procrustes transformation”). phonemes have been attested cross-linguistically (Moran et al., 2014), certain regions of acoustic space are used much more often than others, e.g., the regions conventionally transcribed as [a], [i], and [u]. Human language also seems to prefer in- ventories where phonologically distinct vowels are …

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