# Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model

arXiv: https://arxiv.org/abs/1808.09091 · HTML: https://mlsanity.com/paper/1808.09091/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 11 of 11)

- [Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering](https://mlsanity.com/paper/2106.04016/) (2021)
- [Disfluency Detection with Unlabeled Data and Small BERT Models](https://mlsanity.com/paper/2104.10769/) (2021)
- [LARD: Large-scale Artificial Disfluency Generation](https://mlsanity.com/paper/2201.05041/) (2022)
- [Adversarial Training For Low-Resource Disfluency Correction](https://mlsanity.com/paper/2306.06384/) (2023)
- [Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation](https://mlsanity.com/paper/2408.00205/) (2024)
- [MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup](https://mlsanity.com/paper/2305.12029/) (2023)
- [VAKTA-SETU: A Speech-to-Speech Machine Translation Service in Select Indic Languages](https://mlsanity.com/paper/2305.12518/) (2023)
- [Boosting Disfluency Detection with Large Language Model as Disfluency Generator](https://mlsanity.com/paper/2403.08229/) (2024)
- [DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European Languages](https://mlsanity.com/paper/2310.16749/) (2023)
- [Z-Scores: A Metric for Linguistically Assessing Disfluency Removal](https://mlsanity.com/paper/2509.20319/) (2025)
- [Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication](https://mlsanity.com/paper/2407.08147/) (2024)

## Abstract (arXiv)

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.