Leiden algorithm

Graph-based clustering recovers Zipfian distributions in unsupervised term discovery

A new arXiv preprint argues that K-means and other centre-based clustering methods produce artificially uniform lexicon distributions in unsupervised speech term discovery, due to their bias toward spherical clusters. The authors propose graph-based clustering using the Leiden algorithm as a bottom-up alternative, demonstrating it substantially outperforms K-means, GMM, and BIRCH on word- and syllable-level lexicon discovery across three languages while producing more Zipf-like distributions. The work challenges the dominance of centre-based methods in this subfield of unsupervised speech processing.