We propose a language model based on a precise, linguistically motivated grammar (a hand-crafted Head-driven Phrase Structure Grammar) and a statistical model estimating the probability of a parse tree. The language model is applied by means of an N-best rescoring step, which allows to directly measure the performance gains relative to the baseline system without rescoring. To demonstrate that our approach is feasible and beneficial for non-trivial broad-domain speech recognition tasks, we applied it to a simplified German broadcast-news transcription task. We report a significant reduction in word error rate compared to a state-of-the-art baseline system. .
