# Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives

arXiv: https://arxiv.org/abs/2301.10761 · HTML: https://mlsanity.com/paper/2301.10761/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 114 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Reducing conversational agents' overconfidence through linguistic calibration](https://mlsanity.com/paper/2012.14983/) (2020)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey](https://mlsanity.com/paper/2011.00564/) (2020)
- ["How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations](https://mlsanity.com/paper/2109.13489/) (2021)
- [Recent Trends in Deep Learning Based Personality Detection](https://mlsanity.com/paper/1908.03628/) (2019)
- [Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs](https://mlsanity.com/paper/2106.15684/) (2021)
- [Improving Disfluency Detection by Self-Training a Self-Attentive Model](https://mlsanity.com/paper/2004.05323/) (2020)
- [Fluent Translations from Disfluent Speech in End-to-End Speech Translation](https://mlsanity.com/paper/1906.00556/) (2019)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)
- [End-to-End Speech Recognition and Disfluency Removal](https://mlsanity.com/paper/2009.10298/) (2020)
- [The importance of fillers for text representations of speech transcripts](https://mlsanity.com/paper/2009.11340/) (2020)
- [Teaching BERT to Wait: Balancing Accuracy and Latency for Streaming Disfluency Detection](https://mlsanity.com/paper/2205.00620/) (2022)
- [Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection](https://mlsanity.com/paper/1904.04388/) (2019)
- [Risk-graded Safety for Handling Medical Queries in Conversational AI](https://mlsanity.com/paper/2210.00572/) (2022)
- [On the Role of Style in Parsing Speech with Neural Models](https://mlsanity.com/paper/2010.04288/) (2020)
- [Filler Word Detection and Classification: A Dataset and Benchmark](https://mlsanity.com/paper/2203.15135/) (2022)
- [Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning](https://mlsanity.com/paper/2011.06754/) (2020)
- [DisMo: A Morphosyntactic, Disfluency and Multi-Word Unit Annotator. An Evaluation on a Corpus of French Spontaneous and Read Speech](https://mlsanity.com/paper/1802.02926/) (2018)
- [Multi-Task Learning for Domain-General Spoken Disfluency Detection in Dialogue Systems](https://mlsanity.com/paper/1810.03352/) (2018)
- [Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information](https://mlsanity.com/paper/1704.07287/) (2017)
- [Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields](https://mlsanity.com/paper/1806.07787/) (2018)
- [Increase Apparent Public Speaking Fluency By Speech Augmentation](https://mlsanity.com/paper/1812.03415/) (2018)

## Built on by (most cited first, 5 of 5)

- [Mirages: On Anthropomorphism in Dialogue Systems](https://mlsanity.com/paper/2305.09800/) (2023)
- [Putting Natural in Natural Language Processing](https://mlsanity.com/paper/2305.04572/) (2023)
- [Boosting Disfluency Detection with Large Language Model as Disfluency Generator](https://mlsanity.com/paper/2403.08229/) (2024)
- [Conversational Speech Reveals Structural Robustness Failures in SpeechLLM Backbones](https://mlsanity.com/paper/2509.20321/) (2025)
- [Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models](https://mlsanity.com/paper/2509.20237/) (2025)

## Abstract (arXiv)

Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives influencing Spoken Language Understanding (SLU) on these speech events. This aim of this article is to survey a breadth of perspectives in a holistic way; i.e. from considering underlying (psycho)linguistic theory, to their annotation and consideration in Automatic Speech Recognition (ASR) and SLU systems, to lastly, their study from a generation standpoint. This article aims to present the perspectives in an approachable way to the SLU and Conversational AI community, and discuss moving forward, what we believe are the trends and challenges in each area.