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Statistical language model estimation requires large amounts of domain-specific text, which is difficult to obtain in many languages. We propose techniques which exploit domain-specific text in a resource-rich language to adapt a language model in a resource-deficient language. A primary advantage of our technique is that in the process of cross-lingual language model adaptation, we do not rely on the availability of any machine translation capability. Instead, we assume that only a modest-sized collection of story-aligned document-pairs in the two languages is available. We use ideas from cross-lingual latent semantic analysis to develop a single low-dimensional representation shared by words and documents in both languages, which enables us to (i) find documents in the resource-rich language pertaining to a specific story in the resource-deficient language, and (ii) extract statistics from the pertinent documents to adapt a language model to the story of interest. We demonstrate significant reductions in perplexity and error rates in a Mandarin speech recognition task using this technique.