# Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass

arXiv: https://arxiv.org/abs/2202.05396 · HTML: https://mlsanity.com/paper/2202.05396/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [SEP-28k: A Dataset for Stuttering Event Detection From Podcasts With People Who Stutter](https://mlsanity.com/paper/2102.12394/) (2021)
- [Lessons from Building Acoustic Models with a Million Hours of Speech](https://mlsanity.com/paper/1904.01624/) (2019)
- [Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memory](https://mlsanity.com/paper/1910.12590/) (2019)
- [StutterNet: Stuttering Detection Using Time Delay Neural Network](https://mlsanity.com/paper/2105.05599/) (2021)
- [Improved Robustness to Disfluencies in RNN-Transducer Based Speech Recognition](https://mlsanity.com/paper/2012.06259/) (2020)

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

- [Towards Hierarchical Spoken Language Dysfluency Modeling](https://mlsanity.com/paper/2401.10015/) (2024)
- [Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech](https://mlsanity.com/paper/2405.06150/) (2024)
- [SSDM: Scalable Speech Dysfluency Modeling](https://mlsanity.com/paper/2408.16221/) (2024)
- [Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency Detection](https://mlsanity.com/paper/2505.22029/) (2025)
- [Augmenting Automatic Speech Recognition Models with Disfluency Detection](https://mlsanity.com/paper/2409.10177/) (2024)
- [End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge](https://mlsanity.com/paper/2207.10817/) (2022)
- [AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection](https://mlsanity.com/paper/2406.07256/) (2024)
- [Dysfluent WFST: A Framework for Zero-Shot Speech Dysfluency Transcription and Detection](https://mlsanity.com/paper/2505.16351/) (2025)
- [Missingness-resilient Video-enhanced Multimodal Disfluency Detection](https://mlsanity.com/paper/2406.06964/) (2024)
- [Boli: A dataset for understanding stuttering experience and analyzing stuttered speech](https://mlsanity.com/paper/2501.15877/) (2025)
- [ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers](https://mlsanity.com/paper/2308.15742/) (2023)

## Abstract (arXiv)

It is estimated that around 70 million people worldwide are affected by a speech disorder called stuttering. With recent advances in Automatic Speech Recognition (ASR), voice assistants are increasingly useful in our everyday lives. Many technologies in education, retail, telecommunication and healthcare can now be operated through voice. Unfortunately, these benefits are not accessible for People Who Stutter (PWS). We propose a simple but effective method called 'Detect and Pass' to make modern ASR systems accessible for People Who Stutter in a limited data setting. The algorithm uses a context aware classifier trained on a limited amount of data, to detect acoustic frames that contain stutter. To improve robustness on stuttered speech, this extra information is passed on to the ASR model to be utilized during inference. Our experiments show a reduction of 12.18% to 71.24% in Word Error Rate (WER) across various state of the art ASR systems. Upon varying the threshold of the associated posterior probability of stutter for each stacked frame used in determining low frame rate (LFR) acoustic features, we were able to determine an optimal setting that reduced the WER by 23.93% to 71.67% across different ASR systems.