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Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster

Minu Kim, Hoirin Kim, David R. Mortensen

TL;DR

It is found that the 4K model utilizes a more concentrated encoding that captures shared, robust acoustic signatures such as global energy dynamics such as global energy dynamics, suggesting that massive S3Ms can internalize multiple layers of language history.

Abstract

Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic proximity or surface typological similarities driven by recent expansion or contact, potentially missing deeper genealogical signals. We investigate how scaling linguistic coverage of an S3M-based language identification system from 126 to 4,017 languages influences this topology. Our results reveal a non-linear effect: while phylogenetic recovery remains stagnant up to the 1K scale, the 4K model displays a dramatic qualitative shift, resolving both clear lineages and complex, long-term linguistic contact. Notably, our analysis reveals the emergence of a robust macro-cluster in the Pacific (comprising Papuan, Oceanic, and Australian languages) and investigates its latent drivers. We find that the 4K model utilizes a more concentrated encoding that captures shared, robust acoustic signatures such as global energy dynamics. These findings suggest that massive S3Ms can internalize multiple layers of language history, providing a promising perspective for computational phylogenetics and the study of language contact.

Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster

TL;DR

It is found that the 4K model utilizes a more concentrated encoding that captures shared, robust acoustic signatures such as global energy dynamics such as global energy dynamics, suggesting that massive S3Ms can internalize multiple layers of language history.

Abstract

Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic proximity or surface typological similarities driven by recent expansion or contact, potentially missing deeper genealogical signals. We investigate how scaling linguistic coverage of an S3M-based language identification system from 126 to 4,017 languages influences this topology. Our results reveal a non-linear effect: while phylogenetic recovery remains stagnant up to the 1K scale, the 4K model displays a dramatic qualitative shift, resolving both clear lineages and complex, long-term linguistic contact. Notably, our analysis reveals the emergence of a robust macro-cluster in the Pacific (comprising Papuan, Oceanic, and Australian languages) and investigates its latent drivers. We find that the 4K model utilizes a more concentrated encoding that captures shared, robust acoustic signatures such as global energy dynamics. These findings suggest that massive S3Ms can internalize multiple layers of language history, providing a promising perspective for computational phylogenetics and the study of language contact.
Paper Structure (11 sections, 2 equations, 5 figures, 2 tables)

This paper contains 11 sections, 2 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods of Analysis
  3. Data
  4. Embedding Extraction
  5. Hierarchical Clustering and Evaluation
  6. Dimension-Level Analysis
  7. Results
  8. Phylogenetic Recovery
  9. Linguistic Contact and Areal Patterns
  10. Additional Analysis of the POA Cluster
  11. Conclusion

Figures (5)

  • Figure 1: Evaluation data exposure: MMS-LID-1K vs. 4K. Identical seen/unseen status for 91.8% (45/49) of evaluation languages ensures that performance gaps come from increased linguistic diversity rather than direct exposure.
  • Figure 2: Phylogenetic recovery performance across cluster counts $K \in [2, 20]$ for four model scales. Performance plateaus up to 1K, but shifting to 4K results in a leap that peaks at $K=18$, substantially outperforming all smaller baselines.
  • Figure 3: 4K model bootstrap consensus dendrogram (1,000 replicates). Branch values indicate support percentages. Red boxes highlight clusters reflecting linguistic contact, including the distinction between Austronesian subgroups A and B. Remarkably, 36 of 37 branches with $>50\%$ confidence align with established phylogenetic or areal groupings.
  • Figure 4: POA cluster quality across $K$. Precision plateaus at 0.92 for 128, 256, and 1K models, but the 4K model achieves perfect Precision (1.00), demonstrating a unique capacity to isolate Pacific languages.
  • Figure 5: PCA projections of the embedding spaces for 1K (left) and 4K (right) models across selected groups. The 4K model exhibits more clearly delineated family-level boundaries and a more distinct spatial separation of the POA cluster compared to the 1K model.