Agglomerative Hierarchical Clustering With Dynamic Threshold Tuning

via “agglomerative-clustering-with-dynamic-threshold”

automatic-speech-recognition model by undefined. 27,65,322 downloads.

Unique: Uses a dynamic threshold selection heuristic that adapts to the distribution of pairwise similarities in the embedding space, avoiding manual threshold tuning while maintaining interpretability via dendrogram visualization. Supports multiple linkage methods (complete, average, ward) for different clustering behaviors.

vs others: More interpretable than k-means or spectral clustering (produces dendrogram); automatic speaker count detection vs fixed-k approaches; open-source implementation vs proprietary clustering services.

State-of-the-art speaker diarization toolkit

Unique: Implements dynamic threshold tuning that adapts to embedding statistics (e.g., median pairwise distance, silhouette score), reducing manual hyperparameter tuning. Supports custom linkage criteria and distance metrics, allowing users to experiment with different clustering strategies without reimplementing the algorithm.

vs others: More interpretable than k-means or spectral clustering (dendrogram visualization); more flexible than fixed-threshold approaches by automatically adapting to embedding distributions.