Scaling Self-Supervised Speech Models to 4,000 Languages Reveals Deep Linguistic Relationships and Pacific Language Cluster
Two new studies accepted to Interspeech 2026 demonstrate that large-scale self-supervised speech models can uncover linguistic relationships and recognize rare speech sounds. The first study finds that scaling a speech model to 4,017 languages reveals a Pacific macro-cluster grouping genealogically unrelated languages through shared acoustic features, while the second shows fine-tuned models recognize click consonants from Khoisan languages more accurately than other sounds. These findings suggest self-supervised speech models internalize multiple layers of linguistic information and generalize well across typologically diverse human speech sounds.
Two peer-reviewed studies accepted to Interspeech 2026 explore the capabilities of self-supervised speech models (S3Ms) for linguistic analysis. The first research scales a language identification system from 126 to 4,017 languages and discovers a non-linear effect: while phylogenetic recovery remains flat up to 1,000 languages, the 4,000-language model undergoes a qualitative shift, resolving both genealogical lineages and long-term linguistic contact patterns. Notably, a robust Pacific macro-cluster emerges that groups genealogically unrelated Papuan, Oceanic, and Australian languages, driven by shared acoustic signatures such as global energy dynamics. The second study addresses concerns about underrepresentation of rare phonemes in training data by fine-tuning Wav2Vec2 and HuBERT models on click-rich Khoisan languages (G|ui and West !Xoon). Contrary to expectations, the fine-tuned models recognize clicks more accurately than non-clicks, suggesting that self-supervision enables effective generalization across diverse human speech sounds. Together, these findings indicate that massive self-supervised speech models encode multiple layers of language history and phonetic diversity, offering new perspectives for computational phylogenetics and linguistic research.
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- arXiv cs.CLCenter
Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster
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