# Robust Cross-Etiology and Speaker-Independent Dysarthric Speech Recognition

arXiv: https://arxiv.org/abs/2501.14994 · HTML: https://mlsanity.com/paper/2501.14994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [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)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Speaker adaptation for Wav2vec2 based dysarthric ASR](https://mlsanity.com/paper/2204.00770/) (2022)
- [Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition](https://mlsanity.com/paper/2204.01670/) (2022)
- [Simulating dysarthric speech for training data augmentation in clinical speech applications](https://mlsanity.com/paper/1804.10325/) (2018)
- [Improved Meta Learning for Low Resource Speech Recognition](https://mlsanity.com/paper/2205.06182/) (2022)
- [A Novel Self-training Approach for Low-resource Speech Recognition](https://mlsanity.com/paper/2308.05269/) (2023)
- [Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech](https://mlsanity.com/paper/1905.06533/) (2019)
- [Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation](https://mlsanity.com/paper/2407.18461/) (2024)
- [Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility](https://mlsanity.com/paper/2409.19818/) (2024)

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

- [Low-Burden Data Augmentation for Dysarthric ASR via Zero-Shot Voice Cloning](https://mlsanity.com/paper/2606.19823/) (2026)

## Abstract (arXiv)

In this paper, we present a speaker-independent dysarthric speech recognition system, with a focus on evaluating the recently released Speech Accessibility Project (SAP-1005) dataset, which includes speech data from individuals with Parkinson's disease (PD). Despite the growing body of research in dysarthric speech recognition, many existing systems are speaker-dependent and adaptive, limiting their generalizability across different speakers and etiologies. Our primary objective is to develop a robust speaker-independent model capable of accurately recognizing dysarthric speech, irrespective of the speaker. Additionally, as a secondary objective, we aim to test the cross-etiology performance of our model by evaluating it on the TORGO dataset, which contains speech samples from individuals with cerebral palsy (CP) and amyotrophic lateral sclerosis (ALS). By leveraging the Whisper model, our speaker-independent system achieved a CER of 6.99% and a WER of 10.71% on the SAP-1005 dataset. Further, in cross-etiology settings, we achieved a CER of 25.08% and a WER of 39.56% on the TORGO dataset. These results highlight the potential of our approach to generalize across unseen speakers and different etiologies of dysarthria.