# Artificial Disfluency Detection, Uh No, Disfluency Generation for the Masses

arXiv: https://arxiv.org/abs/2211.09235 · HTML: https://mlsanity.com/paper/2211.09235/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Disfluency Detection using a Bidirectional LSTM](https://mlsanity.com/paper/1604.03209/) (2016)
- [Improving Disfluency Detection by Self-Training a Self-Attentive Model](https://mlsanity.com/paper/2004.05323/) (2020)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [Multi-Task Self-Supervised Learning for Disfluency Detection](https://mlsanity.com/paper/1908.05378/) (2019)
- [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)
- [End-to-End Speech Recognition and Disfluency Removal](https://mlsanity.com/paper/2009.10298/) (2020)
- [Disfluency Detection using Auto-Correlational Neural Networks](https://mlsanity.com/paper/1808.09092/) (2018)
- [Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection](https://mlsanity.com/paper/2205.00620/) (2022)
- [On the Role of Style in Parsing Speech with Neural Models](https://mlsanity.com/paper/2010.04288/) (2020)
- [LARD: Large-scale Artificial Disfluency Generation](https://mlsanity.com/paper/2201.05041/) (2022)

## Built on by (most cited first, 1 of 1)

- [Data Augmentation for Spoken Grammatical Error Correction](https://mlsanity.com/paper/2507.19374/) (2025)

## Abstract (arXiv)

Existing approaches for disfluency detection typically require the existence of large annotated datasets. However, current datasets for this task are limited, suffer from class imbalance, and lack some types of disfluencies that can be encountered in real-world scenarios. This work proposes LARD, a method for automatically generating artificial disfluencies from fluent text. LARD can simulate all the different types of disfluencies (repetitions, replacements and restarts) based on the reparandum/interregnum annotation scheme. In addition, it incorporates contextual embeddings into the disfluency generation to produce realistic context-aware artificial disfluencies. Since the proposed method requires only fluent text, it can be used directly for training, bypassing the requirement of annotated disfluent data. Our empirical evaluation demonstrates that LARD can indeed be effectively used when no or only a few data are available. Furthermore, our detailed analysis suggests that the proposed method generates realistic disfluencies and increases the accuracy of existing disfluency detectors.