Adaptation of lecture speech recognition system with machine translation output

In spoken language translation, integration of the ASR and MT components is critical for good performance. In this paper, we consider the recognition setting where a text translation of each utterance is also available. We present experiments with different ASR system adaptation techniques to exploit MT system outputs. In particular, N-best MT outputs are represented as an utterance-specific language model, which are then used to rescore ASR lattices. We show that this method improves significantly over ASR alone, resulting in an absolute WER reduction of more than 6% for both indomain and out-of-domain acoustic models.