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Cepstral voices serials
Cepstral voices serials





The analysis of pathological voice is a hot topic that has received large attention. In the speech pathology field, on which this work focuses, pathological voices can be evaluated using two approaches that are perceptual analysis and objective analysis. In the objective support of the analysis and the selection of vocal and voice diseases, the automatic evaluation of voice quality based on acoustic analysis stays an efficient tool. The experimental results show that TECCs computed from Gammatone filter bank are more robust in noisy environments than other extracted features, while their performance is practically similar to clean environments. In order to show the robustness of the proposed feature in detection of pathological voices at different White Gaussian noise levels, we compare its performance with results for clean environments. 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.

cepstral voices serials

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.







Cepstral voices serials