In the area of speech signal processing and recognition, application of soft computing techniques is one of the prominent techniques for clustering the overlapping data. Kernel FCM technique is one of the efficient method to cluster the data by computing the cluster centroids. This paper presents and compares the most important clustering techniques like k-means, Fuzzy C means and Kernel Fuzzy C Means algorithms for clustering noisy speech signals. The clustering performances of these techniques are tabulated for homogeneous and heterogeneous speech data sets. This paper highlights the importance of KFCM algorithm for clustering the overlapping data. It also demonstrates the computation time and recognition accuracies of each technique. Our study identifies the KFCM technique performs better than k-means and FCM techniques.
H Y Vani, M A Anusuya, M L Chayadevi
JSS Academy of Technical Education, India
Additive Noise, Clustering, Convolved Noise, Fuzzy C Means (FCM), Heterogeneous Data, Homogeneous Data, K-means, Kernel Fuzzy C Means (KFCM), Principal Component Analysis (PCA), Validity Measures | Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 9 , Issue: 3 )
Date of Publication :
April 2019
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