In domains with insufficient matched training data, language models are often constructed by interpolating component models trained from partially matched corpora. Since the n-grams from such corpora may not be of equal relevance to the target domain, we propose an n-gram weighting technique to adjust the component n-gram probabilities based on features derived from readily available segmentation and metadata information for each corpus. Using a log-linear combination of such features, the resulting model achieves up to a 1.2 % absolute word error rate reduction over a linearly interpolated baseline language model on a lecture transcription task. 1