Abstract
Automatic detection of disfluencies in spoken language is important for making speech recognition output more readable, and for aiding downstream language processing modules. We compare a generative hidden Markov model (HMM)-based approach and two conditional models — a maximum entropy (Maxent) model and a conditional random field (CRF) — for detecting disfluencies in speech. The conditional modeling approaches provide a more principled way to model correlated features. In particular, the CRF approach directly detects the reparandum regions, and thus avoids the use of ad-hoc heuristic rules. We evaluate performance of these three models across two different corpora (conversational speech and broadcast news) and for two types of transcriptions (human transcriptions and recognition output). […]
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