TER-Plus (TERp) is an extended TER evaluation metric incorporating mor- phology, synonymy and paraphrases. There are three new edit operations in TERp: Stem Matches, Synonym Matches and Phrase Substitutions (Para- phrases). In this paper, we propose a TERp-based augmented system com- bination in terms of the backbone se- lection and consensus decoding net- work. Combining the new properties of the TERp, we also propose a two- pass decoding strategy for the lattice- based phrase-level confusion network (CN) to generate the final result.The ex- periments conducted on the NIST2008 Chinese-to-English test set show that our TERp-based augmented system combi- nation framework achieves significant improvements in terms of BLEU and TERp scores compared to the state-of- the-art word-level system combination framework and a TER-based combina- tion strategy.