# Disfluency Detection using Auto-Correlational Neural Networks

arXiv: https://arxiv.org/abs/1808.09092 · HTML: https://mlsanity.com/paper/1808.09092/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2018.

## Built on by (most cited first, 15 of 15)

- [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)
- [Evaluating Document Coherence Modelling](https://mlsanity.com/paper/2103.10133/) (2021)
- [LARD: Large-scale Artificial Disfluency Generation](https://mlsanity.com/paper/2201.05041/) (2022)
- [Filler Word Detection and Classification: A Dataset and Benchmark](https://mlsanity.com/paper/2203.15135/) (2022)
- [Streaming Joint Speech Recognition and Disfluency Detection](https://mlsanity.com/paper/2211.08726/) (2022)
- [Automatic Disfluency Detection from Untranscribed Speech](https://mlsanity.com/paper/2311.00867/) (2023)
- [Span Classification with Structured Information for Disfluency Detection in Spoken Utterances](https://mlsanity.com/paper/2203.16028/) (2022)
- [From Disfluency Detection to Intent Detection and Slot Filling](https://mlsanity.com/paper/2209.08359/) (2022)
- [Transcription free filler word detection with Neural semi-CRFs](https://mlsanity.com/paper/2303.06475/) (2023)
- [Boosting Disfluency Detection with Large Language Model as Disfluency Generator](https://mlsanity.com/paper/2403.08229/) (2024)
- [Artificial Disfluency Detection, Uh No, Disfluency Generation for the Masses](https://mlsanity.com/paper/2211.09235/) (2022)
- [A novel multimodal dynamic fusion network for disfluency detection in spoken utterances](https://mlsanity.com/paper/2211.14700/) (2022)
- [Deep Learning-based automated classification of Chinese Speech Sound Disorders](https://mlsanity.com/paper/2205.11748/) (2022)
- [Toward a Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency](https://mlsanity.com/paper/2312.11509/) (2023)

## Abstract (arXiv)

In recent years, the natural language processing community has moved away from task-specific feature engineering, i.e., researchers discovering ad-hoc feature representations for various tasks, in favor of general-purpose methods that learn the input representation by themselves. However, state-of-the-art approaches to disfluency detection in spontaneous speech transcripts currently still depend on an array of hand-crafted features, and other representations derived from the output of pre-existing systems such as language models or dependency parsers. As an alternative, this paper proposes a simple yet effective model for automatic disfluency detection, called an auto-correlational neural network (ACNN). The model uses a convolutional neural network (CNN) and augments it with a new auto-correlation operator at the lowest layer that can capture the kinds of "rough copy" dependencies that are characteristic of repair disfluencies in speech. In experiments, the ACNN model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score, which is close to the previous best result on this task.