While speaking spontaneously, speakers often make errors such as self-correction or false starts which interfere with the successful application of natural language processing techniques like summarization and machine translation to this data. There is active work on reconstructing this error- ful data into a clean and fluent transcript by identifying and removing these simple errors. Previous research has approximated the potential benefit of conducting word-level reconstruction of simple errors only on those sentences known to have errors. In this work, we explore new approaches for automatically identifying speaker con- struction errors on the utterance level, and quantifytheimpactthatthisinitialstephas on word- and sentence-level reconstruc- tion accuracy.

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