In this paper, we try to introduce the idea of total variability used in speaker recognition to language recognition. In language total variability, we propose two new recognition systems, Language-independent total variability recognition system (LITV) and Languagedependent total variability recognition system (LDTV). Our experiments show that language-independent total factor vector includes the language dependent information, what's more, language-dependent total factor vector contains more language dependent information. These two systems LITV and LDTV can achieve performance similar to that obtained with state-of-the-art approaches. Experiment results on 2007 National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) databases show LDTV gains relative improvement in Equal error rate (EER) of 23.2% and in minimum Decision cost value (minDCF) of 14.2% comparing to LITV in 30-second tasks, and we can obtain further improvement by combining these two new systems with state-of-the-art systems. It leads to relative improvement of 21.1% in EER and 23.1% in minDCF comparing with the performance of the combination of the MMI and the GMM-SVM systems.