The extraction of representative characteristics from voice signals, is the basic step for any application where speech signal processing is performed. In this paper, a comparative analysis of the Mel frequency cepstral coefficients, extracted from samples of voiced and silent speech is exposed. The...

The extraction of representative characteristics from voice signals, is the basic step for any application where speech signal processing is performed. In this paper, a comparative analysis of the Mel frequency cepstral coefficients, extracted from samples of voiced and silent speech is exposed. The classical methodology for extracting Mel frequency cepstral coefficient is evaluated to show the implementation steps and the robustness of such kind of features representing in a unique form the statistical and representative information of time varying signals. To achieve this comprehensive evaluation, first, a contextualization in theoretical terms of the methodology is performed to show the mathematical basis for the coefficient extraction. Each step of the process is analyzed in terms of the representative information of the coefficients. Therefore an evaluation in comparative terms against the previous developments for data interpretation is done. The coefficients are extracted using the classical implementation of the Mel frequency cepstral coefficient feature extraction process in a free software framework implemented in Matlab®. The results show the robustness of the Mel frequency cepstral coefficient algorithm in the characterization of voiced and silent speech signals, which represents an advantage for the construction of any kind of speech processing systems

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