In this paper, we described the process of building a large-scale speech-to-text pipeline. Two target domains, daily conversations and travel-related conversations between two agents, for the English-German language pair (both directions) are examined. The SMT component is built from out-of-domain but freely-available bilingual and monolingual data. We make use of most of the known available resources to examine the effects of unrestricted data and large scale models. A naive baseline delivers solid results in terms of MT-quality. Extending the baseline with context-based translation model features like operations sequence models, higher-order class-based language models, and additional web-scale word-based language models leads to a system that significantly outperforms the baseline. Domain adaption is performed by separately weighting the influence of the out-of-domain subcorpora. This is explored for translation models and language models yielding significant improvements in both cases. Automatic and manual evaluation results are provided for raw MT-quality and ASR+MT-quality.