![]() We evaluate the developed method using mixed voice database composed of recorded voice samples from normophonic or dysphonic speakers. This feature is proposed to identify the pathological voices using a developed neural system of multilayer perceptron (MLP). ![]() Finally, the discrete cosine transform of the log-filtered Teager Energy spectrum is applied. Then, the absolute value of the Teager energy operator of the short-time spectrum is calculated. Firstly, each speech signal frame is passed through a Gammatone or Mel scale triangular filter bank. The robust features which labeled Teager Energy Cepstrum Coefficients (TECCs) are computed in three steps. ![]() The proposed algorithm is based on human auditory processing and the nonlinear Teager-Kaiser energy operator. This paper focuses on a robust feature extraction algorithm for automatic classification of pathological and normal voices in noisy environments.
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