Phonological distances for linguistic typology and the origin of Indo-European languages
Authors: Marius Mavridis, Juan De Gregorio, Raul Toral, David Sanchez
Organizations: Institute for Cross-Disciplinary Physics and Complex Systems IFISC (UIB-CSIC), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain · Universit´e Paris-Saclay, ENS Paris-Saclay, DER de Physique, 91190, Gif-sur-Yvette, France
We show that short-range phoneme dependencies encode large-scale patterns of linguistic relatedness, with direct implications for quantitative typology and evolutionary linguistics. Specifically, using an information-theoretic framework, we argue that phoneme sequences modeled as second-order Markov chains essentially capture the statistical correlations of a phonological system. This finding enables us to quantify distances among 67 modern languages from a multilingual parallel corpus employing a distance metric that incorporates articulatory features of phonemes. The resulting phonological distance matrix recovers major language families and reveals signatures of contact-induced convergence. Remarkably, we obtain a clear correlation with geographic distance, allowing us to constrain a plausible homeland region for the Indo-European family, consistent with the Steppe hypothesis.
Figures & tables
Figure 1: Predictability gain Gu as a function of the phonological block size for French (for which the phoneme inventory size is L=35 ), English ( L=38 ), Russian ( L=47 ) and Greek ( L=30 ).
Figure 2: Left: Predictability gain for English phonological classes as a function of the block size. Consonants can be either all grouped together (Cons) or divided into voiced and unvoiced. Vowels can be grouped in one class (Vow) or classified in terms of openness (open, mid, or close) or backness (front, central or back). The value of L for each curve is the number of categories considered therein (i.e., L=2 for the Cons/Vow curve, L=3 for the Voiced/Unvoiced/Vowel curve, etc.). Right: Same as the left panel but for the French language.
Figure 3: Left: Distributions of English phoneme trigrams. The five most frequent 3-phones are indicated with arrows. Right: Same for French.
Figure 4: Heatmap representation of the phonological distances between all pairs of languages in our sample, calculated on the 3-phone probability distributions under a metric based on articulatory feature vectors. Languages are ordered according to the clusters found by hierarchical clustering using the Ward linkage, visualized as a hierarchical tree.
Figure 5: Same heatmap as Fig. 4 but showing only Indo-European languages Languages. The hierarchical clustering method is now applied to this family, using the Ward linkage, and is displayed as a hierarchical tree. We also order languages according to the clusters.
Figure 6: Wasserstein distance between the 3-phone probability distributions of (left) all languages pairs and (right) IE language pairs only, versus geographic distance dgeo . On each plot, a logarithmic fit is shown in black.
Figure 7: Heatmap for the sum of squared residuals χ2 between the actual and fitted geographical distances that correspond, respectively, to Indo-European language locations and potential family origins. The point with the smallest residual, and consequently the most likely homeland for the Indo-European family, is indicated with a star. We depict its 95% uncertainty region with a dashed circle, calculated from K=2000 random samples of the weights associated with the languages.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Figure 8: Comparison of four different distances between the feature vector representations of selected phoneme pairs. Each curve is normalized to its maximum value. F. (f.) e. d. stands for feature edit distance.
Thomas Lord Department of Computer Science, University of Southern California, USA · School of Electrical Engineering, KAIST, Republic of Korea · Language Technologies Institute, Carnegie Mellon University, USA