To automatically build from scratch the language processing component for a speech synthesis system in a new language a purified text corpora is needed where any words and phrases from other languages are clearly identified or excluded. When using found data and where there is no inherent linguistic knowledge of the language/languages contained in the data, identifying the pure data is a difficult problem. We propose an unsupervised language identification approach based on Latent Dirichlet Allocation where we take the raw n-gram count as features without any smoothing, pruning or interpolation. The Latent Dirichlet Allocation topic model is reformulated for the language identification task and Collapsed Gibbs Sampling is used to train an unsupervised language identification model. We show that such a model is highly capable of identifying the primary language in a corpus and filtering out other languages present.To automatically build from scratch the language processing component for a speech synthesis system in a new language a purified text corpora is needed where any words and phrases from other languages are clearly identified or excluded. When using found data and where there is no inherent linguistic knowledge of the language/languages contained in the data, identifying the pure data is a difficult problem. We propose an unsupervised language identification approach based on Latent Dirichlet Allocation where we take the raw n-gram count as features without any smoothing, pruning or interpolation. The Latent Dirichlet Allocation topic model is reformulated for the language identification task and Collapsed Gibbs Sampling is used to train an unsupervised language identification model. We show that such a model is highly capable of identifying the primary language in a corpus and filtering out other languages present.