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In current language recognition systems, the process of feature extraction from an utterance is usually independent of other utterances. In this paper, we present an approach that build an parallel "relative feature" using the features that have been produced, which is the measurement of the relationship of one utterance with others. The relative feature focuses on "where it is" instead of "what it is", and is more related to the classification than the traditional features. In this work, the method to build and properly use the parallel absolute-relative feature (PARF) language recognition system is also fully explained and developed. To evaluate the system, experiments were carried out on the 2009 National Institute of Standards and Technology language recognition evaluation (NIST LRE) database. The experimental results showed that the relative feature performs better than the absolute feature using a low dimension feature, especially for short test segment. The PARF system yielded 1.84%, 6.04%, 19.89% equal error rate (EER), which achieved a 15.20%, 20.63%, 16.77% relative improvements respectively for 30s, 10s, 3s compared to the baseline system.