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We present an effort to perform topic mixture-based language model adaptation using latent Dirichlet allocation (LDA). We use probabilistic latent semantic analysis (PLSA) to automatically cluster a heterogeneous training corpus, and train an LDA model using the resultant topic-document assignments. Using this LDA model, we then construct topic-specific corpora at the utterance level for interpolation with a background language model during language model adaptation. We also present a novel iterative algorithm for LDA topic inference. Very encouraging results were obtained in preliminary experiments with broadcast news in Mandarin Chinese.