W. Wang, C. Tur, J. Zheng and N. F. Ayan, “Automatic disfluency removal for improving spoken language translation,” in Proc. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), pp. 5214–5217.
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.
Keywords— statistical machine translation, spoken language translation, automatic disfluency detection, broadcast conversation