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Log-linear model combination is the standard approach in LVCSR to combine several knowledge sources, usually an acoustic and a language model. Instead of using a single scaling factor per knowledge source, we make the scaling factor word- and pronunciation-dependent. In this work, we combine three acoustic models, a pronunciation model, and a language model for a Mandarin BN/BC task. The achieved error rate reduction of 2% relative is small but consistent for two test sets. An analysis of the results shows that the major contribution comes from the improved interdependency of language and acoustic model.