arXiv Computation and Language September 15, 2026 By Danel Slabbert, Simon Malan, Herman Kamper

Recovering the Zipfian Distribution in Unsupervised Term Discovery

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The paper investigates unsupervised term discovery in speech, comparing centre-based clustering methods like K‑means with graph‑based clustering using the Leiden algorithm. It finds that graph clustering produces lexicons whose type frequencies follow a Zipfian distribution, outperforming K‑means, GMM, and BIRCH across word‑ and syllable‑level discovery in three languages. Agglomerative clustering with average linkage also performs well but is less efficient and offers less control over the distribution.

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