Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A TAG-based noisy channel model of speech repairs
ACL '04 Proceedings of the 42nd 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
A lexically-driven algorithm for disfluency detection
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Recognizing disfluencies in conversational speech
IEEE Transactions on Audio, Speech, and Language Processing
Exploring Features and Classifiers for Dialogue Act Segmentation
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
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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Previous research has shown that speech disfluencies - speech errors that occur in spoken language - affect NLPsystems and hence need to be repaired or at least marked. This study presents a hybrid approach that uses different detection techniques for this task where each of these techniques is specialized within its own disfluency domain. A thorough investigation of the used disfluency scheme, which was developed by [1], led us to a detection design where basic rule-matching techniques are combined with machine learning approaches. The aim was both to reduce computational overhead and processing time and also to increase the detection performance. In fact, our system works with an accuracy of 92.9% and an F-Score of 90.6% while working faster than real-time.