Graph-Based Clustering Outperforms K-Means for Unsupervised Term Discovery in Speech
Researchers propose using graph-based clustering with the Leiden algorithm as an alternative to K-means for unsupervised term discovery in unlabelled speech, finding it better replicates the Zipfian distribution characteristic of real lexicons. Natural language vocabularies follow Zipf's law, where a few words appear very frequently and most appear rarely, but K-means clustering's bias toward spherical, uniform clusters distorts this property. The findings challenge the dominance of centre-based clustering methods in the field and offer a more linguistically faithful approach to building lexicons from raw speech.
A new preprint from arXiv proposes graph-based clustering as a superior alternative to K-means and other centre-based methods for unsupervised term discovery — the task of segmenting and clustering unlabelled speech into word- or syllable-like units. The core problem identified is that K-means, the dominant approach, has an inductive bias toward spherical clusters that produces an artificially uniform distribution of term frequencies, whereas real lexicons follow a Zipfian (power-law) distribution. The authors use the Leiden graph partitioning algorithm on pairwise similarity graphs of segment embeddings and demonstrate substantially better performance across word- and syllable-level lexicon discovery in three languages. Agglomerative clustering with average linkage also performs competitively but is computationally less efficient and offers less control over the resulting distribution. The study directly challenges the field's reliance on centre-based clustering and positions graph clustering as a practical, linguistically motivated alternative for low-resource and zero-resource speech processing.
What's missing
The study does not specify which three languages were evaluated, leaving open questions about generalizability to typologically diverse or tonal languages. The paper does not address scalability to very large corpora or real-time applications, nor does it compare against more recent neural or self-supervised segmentation baselines beyond the clustering stage. As a preprint, the work has not yet undergone peer review.
What different sources said
- arXiv cs.CLCenter
Recovering the Zipfian Distribution in Unsupervised Term Discovery
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