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The normalized relative compression (NRC) is a recent dissimilarity measure, related to the Kolmogorov complexity. It has been successfully used in different applications, like DNA sequences, images or even ECG (electrocardiographic) signal. It uses a compressor that compresses a target string using exclusively the information contained in a reference string. One possible approach is to use finite-context models (FCMs) to represent the strings. A finite-context model calculates the probability distribution of the next symbol, given the previous k symbols. In this paper, we introduce a generalization of the FCMs, called extended-alphabet finite-context models (xaFCM), that calculates the probability of occurrence of the next d symbols, given the previous k symbols. We perform experiments on two different sample applications using the xaFCMs and the NRC measure: ECG biometric identification, using a publicly …