A maximum entropy approach to natural language processing
Computational Linguistics
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
Journal of VLSI Signal Processing Systems
A speech-first model for repair detection and correction
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Edit detection and parsing for transcribed speech
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
A TAG-based noisy channel model of speech repairs
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Using conditional random fields for sentence boundary detection in speech
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Statistical language modeling for speech disfluencies
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Parsing conversational speech using enhanced segmentation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
A lexically-driven algorithm for disfluency detection
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Corrections to “Automatic Transcription of Conversational Telephone Speech”
IEEE Transactions on Audio, Speech, and Language Processing
Edit disfluency detection and correction using a cleanup language model and an alignment model
IEEE Transactions on Audio, Speech, and Language Processing
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This study describes an approach to edit disfluency detection based on maximum entropy (ME) using contextual features for rich transcription of spontaneous speech. The contextual features contain word-level, chunk-level and sentence-level features for edit disfluency modeling. Due to the problem of data sparsity, word-level features are determined according to the taxonomy of the primary features of the words defined in Hownet. Chunk-level features are extracted based on mutual information of the words. Sentence-level feature are identified according to verbs and their corresponding features. The Improved Iterative Scaling (IIS) algorithm is employed to estimate the optimal weights in the maximum entropy models. Performance on edit disfluency detection and interruption point detection are conducted for evaluation. Experimental results show that the proposed method outperforms the DF-gram approach.