Disfluency correction of spontaneous speech using conditional random fields with variable-length features

This paper presents an approach to detecting and correcting edit disfluency based on conditional random fields with variable-length features. The variable-length features consist of word, chunk and sentence features. Conditional random fields (CRF) are adopted to model the properties of the edit disfluency, including repair, repetition and restart, for edit disfluency detection. For the evaluation of the proposed method, Mandarin conversational dialogue corpus (MCDC) is used. The detection error rate of edit word is 17.3%. Compared with DF-gram, Maximum Entropy and the approach combining language model and alignment model, the proposed approach achieves 11.7%, 8% and 3.9% improvements, respectively. The experimental results show that the proposed model outperforms other methods and efficiently detects and corrects edit disfluency in spontaneous speech.