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In this paper we introduce a novel pattern match neural network architecturethat uses neighbor similarity scores as features, eliminating the need forfeature engineering in a disfluency detection task. We evaluate the approach indisfluency detection for four different speech genres, showing that theapproach is as effective as hand-engineered pattern match features when used onin-domain data and achieves superior performance in cross-domain scenarios.

Robust cross-domain disfluency detection with pattern match networks