Classification of sonorant consonants utilizing empirical mode decomposition
Ashkan Ashrafi, Stanley J. Wenndt · 2014 48th Asilomar Conference on Signals, Systems and Computers · 2014
In this paper, a method to classify nasal utterance among sonorant consonants utilizing empirical mode decomposition (EMD) is introduced. In this method, each audio signal is divided into overlapping 20 millisecond frames. Then each frame's signal is decomposed by using the EMD. Four different features are extracted from each frame to create a vector. These vectors are employed to train a support vector machine (SVM) with radial basis functions. A different set of audio signals are used to validate the SVM model. The results show an overall correct identification rate of 91.19% for nasals and 89.74% for semivowels.