# Stuttering Speech Disfluency Prediction using Explainable Attribution Vectors of Facial Muscle Movements

arXiv: https://arxiv.org/abs/2010.01231 · HTML: https://mlsanity.com/paper/2010.01231/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Analysis and Tuning of a Voice Assistant System for Dysfluent Speech](https://mlsanity.com/paper/2106.11759/) (2021)
- [Fluent: An AI Augmented Writing Tool for People who Stutter](https://mlsanity.com/paper/2108.09918/) (2021)
- [AI-Augmented Behavior Analysis for Children with Developmental Disabilities: Building Towards Precision Treatment](https://mlsanity.com/paper/2102.10635/) (2021)
- [From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech](https://mlsanity.com/paper/2610.19235/) (2026)

## Abstract (arXiv)

Speech disorders such as stuttering disrupt the normal fluency of speech by involuntary repetitions, prolongations and blocking of sounds and syllables. In addition to these disruptions to speech fluency, most adults who stutter (AWS) also experience numerous observable secondary behaviors before, during, and after a stuttering moment, often involving the facial muscles. Recent studies have explored automatic detection of stuttering using Artificial Intelligence (AI) based algorithm from respiratory rate, audio, etc. during speech utterance. However, most methods require controlled environments and/or invasive wearable sensors, and are unable explain why a decision (fluent vs stuttered) was made. We hypothesize that pre-speech facial activity in AWS, which can be captured non-invasively, contains enough information to accurately classify the upcoming utterance as either fluent or stuttered. Towards this end, this paper proposes a novel explainable AI (XAI) assisted convolutional neural network (CNN) classifier to predict near future stuttering by learning temporal facial muscle movement patterns of AWS and explains the important facial muscles and actions involved. Statistical analyses reveal significantly high prevalence of cheek muscles (p<0.005) and lip muscles (p<0.005) to predict stuttering and shows a behavior conducive of arousal and anticipation to speak. The temporal study of these upper and lower facial muscles may facilitate early detection of stuttering, promote automated assessment of stuttering and have application in behavioral therapies by providing automatic non-invasive feedback in realtime.