  This paper presents a model for disfluency detection in spontaneous speech
transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel
Model (NCM) to generate n-best candidate disfluency analyses and a Long
Short-Term Memory (LSTM) language model to score the underlying fluent
sentences of each analysis. The LSTM language model scores, along with other
features, are used in a MaxEnt reranker to identify the most plausible
analysis. We show that using an LSTM language model in the reranking process of
noisy channel disfluency model improves the state-of-the-art in disfluency
detection.
