Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
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
Global inference for sentence compression an integer linear programming approach
Journal of Artificial Intelligence Research
Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
IEEE Transactions on Audio, Speech, and Language Processing
Cross-domain speech disfluency detection
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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We present a novel two-stage technique for detecting speech disfluencies based on Integer Linear Programming (ILP). In the first stage we use state-of-the-art models for speech disfluency detection, in particular, hidden-event language models, maximum entropy models and conditional random fields. During testing each model proposes possible disfluency labels which are then assessed in the presence of local and global constraints using ILP. Our experimental results show that by using ILP we can improve the performance of our models with negligible cost in processing time. The less training data is available the larger the improvement due to ILP.