Self-Tuning Spectral Clustering for Speaker Diarization
<|reference_start|>Self-Tuning Spectral Clustering for Speaker Diarization: Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian. In this study, we present a novel pruning algorithm to create a sparse affinity matrix called \emph{spectral clustering on p-neighborhood retained affinity matrix} (SC-pNA). Our method improves on node-specific fixed neighbor selection by allowing a variable number of neighbors, eliminating the need for external tuning data as the pruning parameters are derived directly from the affinity matrix. SC-pNA does so by identifying two clusters in every row of the initial affinity matrix, and retains only the top $p\%$ similarity scores from the cluster containing larger similarities. Spectral clustering is performed subsequently, with the number of clusters determined as the maximum eigengap. Experimental results on the challenging DIHARD-III dataset highlight the superiority of SC-pNA, which is also computationally more efficient than existing auto-tuning approaches.<|reference_end|>
@article{raghav2024self-tuning,
    title={Self-Tuning Spectral Clustering for Speaker Diarization},
    author={Nikhil Raghav, Avisek Gupta, Md Sahidullah, Swagatam Das},
    journal={arXiv preprint arXiv:2410.00023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00023},
    primaryClass={eess.SP cs.LG cs.SD eess.AS}
}