Publication

COMBINATION OF AGGLOMERATIVE AND SEQUENTIAL CLUSTERING FOR SPEAKER DIARIZATION

Hervé Bourlard
2007
Reports

Abstract

This paper aims at investigating the use of sequential clustering for speaker diarization. Conventional diarization systems are based on parametric models and agglomerative clustering. In our previous work we proposed a non-parametric method based on the agglomerative Information Bottleneck for very fast diarization. Here we consider the combination of sequential and agglomerative clustering for avoiding local maxima of the objective function and for purification. Experiments are run on the RT06 eval data. Sequential Clustering with oracle model selection can reduce the speaker error by $10\%$ w.r.t. agglomerative clustering. When the model selection is based on Normalized Mutual Information criterion, a relative improvement of $5\%$ is obtained using a combination of agglomerative and sequential clustering.

Official source

https://infoscience.epfl.ch/entities/publication/f13416b7-058d-428c-b4df-39e742d1c7ea

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Hervé Bourlard
2007
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/f13416b7-058d-428c-b4df-39e742d1c7ea

About this result

Related concepts (30)

Hierarchical clustering

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

Nonparametric statistics

Nonparametric statistics is the type of statistics that is not restricted by assumptions concerning the nature of the population from which a sample is drawn. This is opposed to parametric statistics, for which a problem is restricted a priori by assumptions concerning the specific distribution of the population (such as the normal distribution) and parameters (such the mean or variance).

Single-linkage clustering

In statistics, single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other. This method tends to produce long thin clusters in which nearby elements of the same cluster have small distances, but elements at opposite ends of a cluster may be much farther from each other than two elements of other clusters.

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