Nowadays official documents have to be made available in many languages, like for example in the EU with its 20 official languages. Therefore, the need for effective tools to aid the multitude of human translators in their work becomes easily apparent. An ASR system, enabling the human translator to speak his translation in an unrestricted manner, instead of typing it, constitutes such a tool. In this work we improve the recognition performance of such an ASR system on the target language of the human translator by taking advantage of an either written or spoken source language representation. To do so, machine translation techniques are used to translate between the different languages and then the involved ASR systems are biased towards the gained knowledge. We present an iterative approach for ASR improvement and outperform our baseline system by a relative word error rate reduction of 35.8%/29.9% in the case of a written/spoken source language representation. Further, we show how multiple target languages, as for example provided by different simultaneous translators during European Parliament debates, can be incorporated into our system design for an improvement of all involved ASR systems