Abstract
In this paper, various temporal features (i.e., zero crossing rate and short-time energy) and spectral features (spectral flux and spectral centroid) have been derived from the Teager energy operator (TEO) profile of the speech waveform. The efficacy of these features has been analyzed for the classification of normal and dysphonic voices by comparing their performance with the features derived from the linear prediction (LP) residual and the speech waveform. In addition, the effectiveness of fusing these features with state-of-the-art Mel frequency cepstral coefficients (MFCC) feature-set has also been investigated to understand whether these features provide complementary results. The classifier that has been used is the 2nd order polynomial classifier, with experiments being carried out on a subset of the Massachusetts Eye and Ear Infirmary (MEEI) database.
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Patil, H.A., Baljekar, P.N., Basu, T.K. (2012). Novel Temporal and Spectral Features Derived from TEO for Classification Normal and Dysphonic Voices. In: Sambath, S., Zhu, E. (eds) Frontiers in Computer Education. Advances in Intelligent and Soft Computing, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27552-4_76
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