Abstract
In this paper, a method based on cepstra derived from the 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. Unlike conventional mel frequency cepstral coefficients (MFCCs) derived from the power spectrum, MFCCs computed from the product spectrum, namely MFPSCCs are fed to a linear discriminant analysis (LDA) based classifier for the detection and classification of nasalized vowels. The performance of nasalized vowel detection and classification based on some of the state-of-the-art features, namely MFCCs, A1–P1 are compared with that of the proposed feature using not only LDA based classifier but also support vector machine based classifier. A detail simulation results on TIMIT database show that the proposed cepstral features derived from the product spectrum outperform the state-of-the-art features in the task of detecting and classifying nasalized vowels in clean as well as different noisy conditions.
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Najnin, S., Shahnaz, C. A detection and classification method for nasalized vowels in noise using product spectrum based cepstra. Int J Speech Technol 18, 97–111 (2015). https://doi.org/10.1007/s10772-014-9225-9
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DOI: https://doi.org/10.1007/s10772-014-9225-9