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One successful approach to language recognition is to focus on the most discriminative high level features of languages, such as phones and words. In this paper, we applied a similar approach to acoustic features using a single GMM-tokenizer followed by discriminatively trained language models. A feature selection technique based on the Support Vector Machine (SVM) is used to model higher order n-grams. Three different ways to build this tokenizer are explored and compared using discriminative uni-gram and generative GMM-UBM. A discriminative uni-gram using very large GMM tokenizer with 24,576 components yields an EER of 1.66%, rising to 0.71% when fused with other acoustic approaches, on the NIST03 LRE 30s evaluation.