@InProceedings{jamshidlou-johnson:2017:Short,
  author    = {Jamshid Lou, Paria  and  Johnson, Mark},
  title     = {Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {547--553},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/P17-2087}
}