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This paper compares techniques to combine diverse parallel corpora for domain-specific phrase-based SMT system training. We address a common scenario where little in-domain data is available for the task, but where large background models exist for the same language pair. In particular, we focus on phrase table fill-up: a method that effectively exploits background knowledge to improve model coverage, while preserving the more reliable information coming from the in-domain corpus. We present experiments on an emerging transcribed speech translation task – the TED talks. While performing similarly in terms of BLEU and NIST scores to the popular log-linear and linear interpolation techniques, filled-up translation models are more compact and easy to tune by minimum error training.