Detection and Classification of Nasalized Vowels in Noise Based on Cepstra Derived from Differential Product Spectrum

Abstract

In this paper, a method based on cepstra derived from the differential product spectrum is developed for the detection and classification of nasalized vowels with varying degree of nasalization. Conventionally, features for detecting and classifying nasalized vowels are derived considering magnitude spectrum only, ignoring the phase spectrum. Exploiting the power spectrum and the group delay function of a band-limited vowel, the product spectrum is defined thus incorporating the information of both magnitude and phase spectra. The product spectrum is then differentiated with respect to frequency to obtain differential product spectrum (DPrS) that is argued to provide more noise robustness in the presence of noise. Unlike conventional mel-frequency cepstral coefficient (MFCC), MFCCs computed from the differential product spectrum, namely MFDPrSCCs, are fed to a linear discriminant analysis-based classifier for the detection and classification of nasalized vowels. Detailed simulation results on TIMIT database show that the proposed cepstral features derived from the differential product spectrum are capable of outperforming the cepstral features derived from the conventional power spectrum in the task of detecting and classifying nasalized vowel not only in clean condition but also in different noisy condition with varying signal to noise ratio.