Abstract
Previous research has shown that speech disfluencies - speech errors that occur in spoken language - affect NLP systems 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.
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