State-space dynamics distance for clustering sequential data
Pattern Recognition
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We propose a new algorithm for sequence segmentation based on recent advances in semi-parametric sequence clustering. This approach implies the use of model-based distance measures between sequences, as well as a variant of spectral clustering specially tailored for segmentation. The method is highly flexible since it allows for the use of any probabilistic generative model for the individual segments. The performance of the proposed algorithm is demonstrated using both a synthetic dataset and a speaker segmentation task.