Speaker Identifi
Speaker
Identification based on Hybrid Clustering and Radial Basis Function
Yap Teck Ann1,
Mohd Shafry Mohd Rahim1, Ayman Altameem2, Amjad Rehman1,
Ismail Mat Amin1, Tanzila Saba3
1. Faculty of Computer Science and Information
Systems University Teknologi Malaysia, 81310 Skudai Malaysia
2. College of Applied Studies and Community
Services King Saud University Riyadh KSA
3College of Computer Science and Engineering Salman
Abdul Aziz University Alkharj KSA
Abstract: Speaker
identification is the computing task to identify an unknown identity based on
the voice. A good speaker identification system must have a high accuracy rate
to avoid invalid identity. Despite of last few decades efforts, accuracy rate
in speaker identification is still low. In this paper, we propose a hybrid
approach of unsupervised and supervised learning i.e. subtractive clustering
and radial basis function(Sub-RBF).The proposed fused technique yields
promising results because subtractive clustering is able to solve the initial
guesses of cluster center and difficulty level to determine the number of
cluster. Besides that, RBF has simple network structure and faster learning
algorithm. In RBF input to output map uses the local approximations which will
combine the linear approximations and causes the linear combinations with less
weights. RBF neural network model uses subtractive clustering algorithm to
select the hidden node centers for high training speed. In the meantime, the
RBF network is trained with a regularization term so as to minimize the
variances of the nodes in the hidden layer and to perform accurate prediction.
Promising results are achieved to identify speaker using proposed fused
approach.
[Ann Y. T, Rahim M.S.M., Altameem A, Rehman A, Amin,
I, M. Saba T. Speaker Identification based on Hybrid Clustering
and Radial Basis Function. J Am Sci 2012;8(10):71-75].
(ISSN: 1545-1003).http://www.jofamericanscience.org. 12
Keywords: Speaker identification, features extraction,
clustering, radial basis function, K-means and fuzzy c-means. Full Text 12
