Abstract
This paper makes a comparison of two methods in speech processing that aims to obtain cepstral coefficients that represent characteristics of each speaker, in order to obtain a greater difference between each speaker (to build a representative model, in a next step), traditionally feature extraction is performed through a single periodic window function (generally hamming windows), we compare the traditional method with multi-taper, which has been used in verification speakers tasks and language recognition. The results obtained using 40 speakers indicate a reduction of the variance by using multi-taper and better separation between speakers; with this we have more robust coefficients. To feature extractions we use Mel Frequency Cepstral Coefficients.
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