Edit disfluency detection and correction using a cleanup language model and an alignment model

This investigation presents a novel approach to detecting and correcting the edit disfluency in spontaneous speech. Hypothesis testing using acoustic features is first adopted to detect potential interruption points (IPs) in the input speech. The word order of the cleanup utterance is then cleaned up based on the potential IPs using a class-based cleanup language model, the deletable region and the correction are aligned using an alignment model. Finally, log linear weighting is applied to optimize the performance. Using the acoustic features, the IP detection rate is significantly improved especially in recall rate. Based on the positions of the potential IPs, the cleanup language model and the alignment model are able to detect and correct the edit disfluency efficiently. Experimental results demonstrate that the proposed approach has achieved error rates of 0.33 and 0.21 for IP detection and edit word deletion, respectively.