%0 Journal Article
%T Disorder Speech Clustering For Clinical Data Using Fuzzy C-Means Clustering And Comparison With SVM Classification
%A C.R.Bharathi
%A Dr.V. Shanthi
%J Indian Journal of Computer Science and Engineering
%D 2012
%I Engg Journals Publications
%X Speech is the most vital skill of communication. Stammering is speech which is hesitant, stumbling, tense or jerky to the extent that it causes anxiety to the speaker. In the existing system, there are many effective treatments for the problem of stammering. Speech-language therapy is the treatment for most kids with speech and/or language disorders. In this work, mild level of mental retardation (MR) children speech samples were taken for consideration. The proposed work is, the acute spot must be identified for affording speech training to the speech disordered children. To begin with the proposed work, initially Clustering of speech is done using Fuzzy C-means Clustering Algorithm. Feature Extraction is implemented using Mel Frequency Cepstrum Coefficients (MFCC) and dimensionality reduction of features extracted is implemented using Principal Component Analysis (PCA). Finally the features were clustered using Fuzzy C-Means algorithm and compared with SVM classifier output[13].
%K speech
%K stammering
%K Mel Frequency Cepstrum Coefficients (MFCC)
%K Principal Component Analysis (PCA)
%K clustering
%U http://www.ijcse.com/docs/INDJCSE12-03-05-029.pdf