Noisy Speech Recognition Using Kernel Fuzzy C Means

In the area of voice recognition, soft computing technique is a prominent method to identify and cluster speaker variability’s in the speech signal. But whenever the signal is convoluted by a noisy signal standard FCM method fails to give the good results. To overcome this, Kernel FCM (KFCM) is used...

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Bibliographic Details
Published inCognitive Computing and Information Processing Vol. 801; pp. 324 - 330
Main Authors Vani, H. Y., Anusuya, M. A.
Format Book Chapter
LanguageEnglish
Published Singapore Springer Singapore Pte. Limited 2018
Springer Singapore
SeriesCommunications in Computer and Information Science
Subjects
Additive noise
Cepstral Coefficients (MFCC)
Computation Complexity (CC)
Convolution noise
Kernel Fuzzy C Means (KFCM)
Principal Component Analysis (PCA)
Soft computing technique
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Summary:In the area of voice recognition, soft computing technique is a prominent method to identify and cluster speaker variability’s in the speech signal. But whenever the signal is convoluted by a noisy signal standard FCM method fails to give the good results. To overcome this, Kernel FCM (KFCM) is used in this paper. PCA helps in reducing the features of convoluted signal. The recognition results are compared with and without applying PCA using KFCM function and the same is presented for word recognition rate.
ISBN:9789811090585
9811090580
ISSN:1865-0929
1865-0937
DOI:10.1007/978-981-10-9059-2_29