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Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : International Journal of Computer Science and Information Technology & Security
Source : International Journal of Computer Science and Information Technology & Security (2012)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Verified : Yes
Year : 2012
Abstract : Cleft lip and palate is one of the main reason for speech disorders in children. The major one among these speech disorders is hypernasality caused due to abnormal nasal resonance. For the detection of hypernasality, we commonly analyse normal vowels and nasalized vowels. In this paper, we have done a comparative study on two feature extraction methods such as Mel Frequency Cepstral Coefficients (MFCC) and Bessel Frequency Cepstral Coefficients (BFCC). We have trained these features using Gaussian Mixture Model (GMM) of various orders. The classification of hypernasal speech is performed with upto 50% for 'A', 87.5% for 'E', 100% for 'I', 'O' and 'U' for MFCC with GMM of various orders. For BFCC with GMM of various orders, performance upto 87.5% for 'A', 75% for 'E', 75% for 'I', 100% for 'O' and 'U' is achieved. We have given an analysis hypernasal and normal speech vowel using its spectrogram.
Cite this Research Publication : N. George, Soman, K. P., S, S. Kumar, and G, R., “A Performance Study on Detection of Hypernasality in Children using MFCC, BFCC and various orders of GMM classifier”, International Journal of Computer Science and Information Technology & Security, 2012.