Speaker identification based on subtractive clustering algorithm with estimating number of clusters

Authors:
Younjeong Lee;Ki Yong Lee;Jaeyeol Rheem
Affiliations:
School of Electronic Engineering, SoongSil University, Seoul, Korea;School of Electronic Engineering, SoongSil University, Seoul, Korea;School of Information and Technology, Korea University of Technology and Education, Byungcheon, Cheonan, Korea
Venue:
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Year:
2005

Citing 6
Cited 0

Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
On finding the number of clusters

Pattern Recognition Letters
An empirical comparison of four initialization methods for the K-Means algorithm

Pattern Recognition Letters
Mutual Information Theory for Adaptive Mixture Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity relations and fuzzy orderings

Information Sciences: an International Journal
Efficient speaker identification based on robust VQ-PCA

ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a new clustering algorithm that performs clustering the feature vectors for the speaker identification. Unlike typical clustering approaches, the proposed method does the clustering without the initial guesses of locations of the cluster centers and a priori information about the number of clusters. Cluster centers are obtained incrementally by adding one cluster center at a time through the subtractive clustering algorithm. The number of clusters is obtained by investigating the mutual relationship between clusters. The experimental results show the effectiveness of the proposed algorithm as compared with the conventional methods.