Self-Tuning Spectral Clustering for Speaker Diarization

• URL: http://arxiv.org/abs/2410.00023v1
• Date: Mon, 16 Sep 2024 05:07:47 GMT
• Title: Self-Tuning Spectral Clustering for Speaker Diarization
• Authors: Nikhil Raghav, Avisek Gupta, Md Sahidullah, Swagatam Das,
• Abstract summary: We present a novel pruning algorithm to create a sparse affinity matrix called emphspectral 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. Experimental results on the challenging DIHARD-III dataset highlight the superiority of SC-pNA.
• Score: 19.487611671681968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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