Universal Background Sparse Coding and Multilayer Bootstrap Network for Speaker Clustering

We apply multilayer bootstrap network (MBN) to speaker clustering. The proposed method first extracts supervectors by a universal background model, then reduces the dimension of the high-dimensional supervectors by MBN, and finally conducts speaker clustering by clustering the low-dimensional data. We also propose an MBN-based universal background model, named universal background sparse coding. The comparison results demonstrate the effectiveness and robustness of the proposed method.