Phonological distances for linguistic typology and the origin of Indo-European languages

• URL: http://arxiv.org/abs/2604.11565v1
• Date: Mon, 13 Apr 2026 14:48:15 GMT
• Title: Phonological distances for linguistic typology and the origin of Indo-European languages
• Abstract summary: We show that short-range phoneme dependencies encode large-scale patterns of linguistic relatedness.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Pretrained Multilingual Transformers Reveal Quantitative Distance Between Human Languages [25.106812852291622]
  We introduce a method that leverages pretrained multilingual language models as systematic instruments for linguistic measurement.<n>We show that the spontaneously emerged attention mechanisms of these models provide a robust, tokenization-agnostic measure of cross-linguistic distance.<n>Our results establish a principled foundation for testing linguistic hypotheses using artificial neural networks.
  arXiv  Detail & Related papers  (2026-03-18T16:50:23Z)
• Modality Matching Matters: Calibrating Language Distances for Cross-Lingual Transfer in URIEL+ [4.262015666551064]
  We introduce a framework for type-matched language distances.<n>We propose novel, structure-aware representations for each distance type.<n>We unify these signals into a robust, task-agnostic composite distance.
  arXiv  Detail & Related papers  (2025-10-22T03:59:19Z)
• Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically [58.019484208091534]
  Cross-lingual alignment in pretrained language models (LMs) has enabled efficient transfer in text-based LMs.<n>It remains an open question whether findings and methods from text-based cross-lingual alignment apply to speech.
  arXiv  Detail & Related papers  (2025-05-26T07:21:20Z)
• Exploring language relations through syntactic distances and geographic proximity [0.4369550829556578]
  We explore linguistic distances using series of parts of speech (POS) extracted from the Universal Dependencies dataset. We find definite clusters that correspond to well known language families and groups, with exceptions explained by distinct morphological typologies.
  arXiv  Detail & Related papers  (2024-03-27T10:36:17Z)
• Patterns of Persistence and Diffusibility across the World's Languages [3.7055269158186874]
  Colexification is a type of similarity where a single lexical form is used to convey multiple meanings. We shed light on the linguistic causes of cross-lingual similarity in colexification and phonology. We construct large-scale graphs incorporating semantic, genealogical, phonological and geographical data for 1,966 languages.
  arXiv  Detail & Related papers  (2024-01-03T12:05:38Z)
• Quantifying the Dialect Gap and its Correlates Across Languages [69.18461982439031]
  This work will lay the foundation for furthering the field of dialectal NLP by laying out evident disparities and identifying possible pathways for addressing them through mindful data collection.
  arXiv  Detail & Related papers  (2023-10-23T17:42:01Z)
• A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Space [61.18554842370824]
  In cross-lingual language models, representations for many different languages live in the same space. We compute a task-based measure of cross-lingual alignment in the form of bitext retrieval performance. We examine a range of linguistic, quasi-linguistic, and training-related features as potential predictors of these alignment metrics.
  arXiv  Detail & Related papers  (2021-09-13T21:05:37Z)
• Probing Multilingual BERT for Genetic and Typological Signals [28.360662552057324]
  We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages. We employ the language distances to infer and evaluate language trees, finding that they are close to the reference family tree in terms of quartet tree distance.
  arXiv  Detail & Related papers  (2020-11-04T00:03:04Z)
• Constructing a Family Tree of Ten Indo-European Languages with Delexicalized Cross-linguistic Transfer Patterns [57.86480614673034]
  We formalize the delexicalized transfer as interpretable tree-to-string and tree-to-tree patterns. This allows us to quantitatively probe cross-linguistic transfer and extend inquiries of Second Language Acquisition.
  arXiv  Detail & Related papers  (2020-07-17T15:56:54Z)
• Bridging Linguistic Typology and Multilingual Machine Translation with Multi-View Language Representations [83.27475281544868]
  We use singular vector canonical correlation analysis to study what kind of information is induced from each source. We observe that our representations embed typology and strengthen correlations with language relationships. We then take advantage of our multi-view language vector space for multilingual machine translation, where we achieve competitive overall translation accuracy.
  arXiv  Detail & Related papers  (2020-04-30T16:25:39Z)
• Universal Phone Recognition with a Multilingual Allophone System [135.2254086165086]
  We propose a joint model of language-independent phone and language-dependent phoneme distributions. In multilingual ASR experiments over 11 languages, we find that this model improves testing performance by 2% phoneme error rate absolute. Our recognizer achieves phone accuracy improvements of more than 17%, moving a step closer to speech recognition for all languages in the world.
  arXiv  Detail & Related papers  (2020-02-26T21:28:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.