Use of glottal inverse filtering for asthma and HIE infant cries classification

Abstract

In this paper, feature derived from the glottal inverse filtering of the speech signal is used for classification of pathological infant cries. Glottal inverse filtering is used to estimate the glottal volume velocity waveform (i.e., the source of voicing for infant cry). Here, GIF is used to separate the glottal source and vocal tract filter. The source and the filter features are used for pathological cries classification. Through the experimental results, importance of both the features in cry classification is investigated. State-of-the-art feature set, viz., Mel Frequency Cepstral Coefficients (MFCC) is also used to compare performance of the proposed feature set. Experimental results show classification accuracy of 76.28 % with the proposed features as opposed to state-of-the-art, MFCC feature which shows classification accuracy of 71.13 %. Fusion of the proposed feature set with MFCC gives classification accuracy of 78.35 % indicating that proposed feature captures the complimentary information in infant cry signal. All experiments were conducted with SVM classifier with radial basis function kernel.