Learning Deep Representations By Multilayer Bootstrap Networks For Speaker Diarization

Abstract

The performance of speaker diarization is strongly affected by its clustering algorithm at the test stage. However, it is known that clustering algorithms are sensitive to random noises and small variations, particularly when the clustering algorithms themselves suffer some weaknesses, such as bad local minima and prior assumptions. To deal with the problem, a compact representation of speech segments with small within-class variances and large between-class distances is usually needed. In this paper, we apply an unsupervised deep model, named multilayer bootstrap network (MBN), to further process the embedding vectors of speech segments for the above problem. MBN is an unsupervised deep model for nonlinear dimensionality reduction. Unlike traditional neural network based deep model, it is a stack of \(k\)-centroids clustering ensembles, each of which is trained simply by random resampling of data and one-nearest-neighbor optimization. We construct speaker diarization systems by combin

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• arxiv keyli2019learning

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