Computer Science ›› 2018, Vol. 45 ›› Issue (4): 278-284.doi: 10.11896/j.issn.1002-137X.2018.04.047

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Automatic Detection of Hypernasality Grades Based on Discrete Wavelet Transformation and Cepstrum Analysis

ZHAO Li-bo, LIU Qi, FU Fang-ling and HE Ling   

  • Online:2018-04-15 Published:2018-05-11

Abstract: This paper proposed an automatic hypernasality grades classification algorithm in cleft palate speech based on discrete wavelet decomposition coefficients and cepstrum analysis.Currently,the widely used features to classify hypernasality grades include MFCC,Teager energy,Shannon energy and so on.However,the classification accuracy is low,and the computation amount is large.The speech data tested in this work include 1789 Mandarin syllables with the final \a\,which are spoken by cleft palate patients with four grades of hypernasality.The wavelet decomposition coefficientcepstrum was extracted as the acoustic feature,and then KNN classifier was applied to identify four grades of hyperna-sality.The classification performance was compared with five acoustic features:MFCC,LPCC,pitch period,formant and short-time energy.Meanwhile,the performance of KNN was compared with SVM classifier.The experimental results indicate that the recognition accuracy obtained by using wavelet decomposition coefficient cepstrum feature is higher than that obtained by using five classical acoustics features.The classification accuracy is higher when using KNN than SVM classifier.Recognition accuracy obtained by using wavelet decomposition coefficient cepstrum feature combined with KNN is 91.67%,and 87.60% combined with SVM.Recognition accuracy using classical acoustics features combined with KNN is only 21.69%~84.54%,and 30.61%~78.24% combined with SVM.

Key words: Cleft palate,Hypernasality,Recognition system,Wavelet decomposition coefficient cepstrum

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