# Speech Recognition-based Feature Extraction for Enhanced Automatic Severity Classification in Dysarthric Speech

arXiv: https://arxiv.org/abs/2412.03784 · HTML: https://mlsanity.com/paper/2412.03784/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Assessing ASR Model Quality on Disordered Speech using BERTScore](https://mlsanity.com/paper/2209.10591/) (2022)
- [Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil](https://mlsanity.com/paper/2209.12942/) (2022)
- [Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task Learning](https://mlsanity.com/paper/2210.15387/) (2022)
- [Inappropriate Pause Detection In Dysarthric Speech Using Large-Scale Speech Recognition](https://mlsanity.com/paper/2402.18923/) (2024)

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

- [Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS](https://mlsanity.com/paper/2508.05102/) (2025)

## Abstract (arXiv)

Due to the subjective nature of current clinical evaluation, the need for automatic severity evaluation in dysarthric speech has emerged. DNN models outperform ML models but lack user-friendly explainability. ML models offer explainable results at a feature level, but their performance is comparatively lower. Current ML models extract various features from raw waveforms to predict severity. However, existing methods do not encompass all dysarthric features used in clinical evaluation. To address this gap, we propose a feature extraction method that minimizes information loss. We introduce an ASR transcription as a novel feature extraction source. We finetune the ASR model for dysarthric speech, then use this model to transcribe dysarthric speech and extract word segment boundary information. It enables capturing finer pronunciation and broader prosodic features. These features demonstrated an improved severity prediction performance to existing features: balanced accuracy of 83.72%.