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

arXiv: https://arxiv.org/abs/2603.07238 · HTML: https://mlsanity.com/paper/2603.07238/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Builds on (most cited first, 5 of 5)

- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis](https://mlsanity.com/paper/2501.06810/) (2025)
- [Neighbors and relatives: How do speech embeddings reflect linguistic connections across the world?](https://mlsanity.com/paper/2506.08564/) (2025)
- [Characterization of Speech Similarity Between Australian Aboriginal and High-Resource Languages: A Case Study on Dharawal](https://mlsanity.com/paper/2509.01419/) (2025)

## Built on by (most cited first, 1 of 1)

- [Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection](https://mlsanity.com/paper/2609.16458/) (2026)

## Abstract (arXiv)

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 an S3M-based language identification system from 126 to 4,017 languages reshapes this topology, and find a non-linear effect: phylogenetic recovery stays flat up to the 1K scale, but the 4K model undergoes a qualitative shift, resolving both clear lineages and long-term linguistic contact. Most strikingly, a robust Pacific macro-cluster emerges, grouping genealogically unrelated Papuan, Oceanic, and Australian languages, and we trace its driver to a concentrated encoding that captures shared acoustic signatures such as global energy dynamics. These results suggest that massive S3Ms internalize multiple layers of language history, offering a promising perspective for computational phylogenetics and the study of language contact.