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Statistical machine translation (SMT) systems for spoken languages suffer from conversational speech phenomena, in particular, the presence of speech disfluencies. We examine the impact of disfluencies from broadcast conversation data on our hierarchical phrase-based SMT system and implement automatic disfluency removal approaches for cleansing the MT input. We evaluate the efficacy of proposed approaches and investigate the impact of disfluency removal on SMT performance across different disfluency types. We show that for translating Mandarin broadcast conversational transcripts into English, our automatic disfluency removal approaches could produce significant improvement in BLEU and TER.