A filter that introduces inter-frame information into the voice features set is proposed in this paper. The filter adds the autocorrelations of the cepstral coefficients to the set of characteristics used for training and recognition. Those autocorrelations should not depend on the environment conditions. Because they should only depend on the information to recognize, a normalization of that inter-frame information is convenient. The filter defined implements this normalization by transforming the autocorrelations into a normalized domain defined with clean adaptation data. This temporal processing of the features is added to the Histogram Equalization of the cepstral coefficients (HEQ) used to normalize the MFCCs. An analysis is done about the most effective domain (original MFCCS or equalized MFCCs) on which the temporal processing should be executed. Performance results for the proposed algorithm are presented for AURORA2 and AURORA4 databases.