Spoken language translation (SLT) combines automatic speech recognition (ASR) and machine translation (MT). State-of-the-art SLT systems normally require careful tuning of the parameters of both the ASR and the MT components. To achieve reasonable performance the ASR components are normally required to demonstrate robust performance (with low word error rates, WER), upon which a pipeline approach is adopted to link the ASR and the MT components. In more practical scenarios, an SLT system has to deal with more varied inputs. In a situation with mismatched input or domain, the automatic transcript may have much higher WERs. At this point little is known on what types of errors in high WER scenarios cause specific degradation in MT performance. This research focuses on improving the SLT system performance by means of system integration. By modelling the information flow between the ASR and the MT components, the ASR output can be filtered and/or adapted to alleviate the conditions of model mismatch. MT system can also be tuned to the filtered, coupling output.