Universal Background Sparse Coding and Multilayer Bootstrap Network for Speaker Clustering

Xiao-Lei Zhang


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.


DOI: 10.21437/Interspeech.2016-65

Cite as

Zhang, X. (2016) Universal Background Sparse Coding and Multilayer Bootstrap Network for Speaker Clustering. Proc. Interspeech 2016, 1858-1862.

Bibtex
@inproceedings{Zhang2016,
author={Xiao-Lei Zhang},
title={Universal Background Sparse Coding and Multilayer Bootstrap Network for Speaker Clustering},
year=2016,
booktitle={Interspeech 2016},
doi={10.21437/Interspeech.2016-65},
url={http://dx.doi.org/10.21437/Interspeech.2016-65},
pages={1858--1862}
}