# Personalizing ASR for Dysarthric and Accented Speech with Limited Data

arXiv: https://arxiv.org/abs/1907.13511 · HTML: https://mlsanity.com/paper/1907.13511/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 30 of 45)

- [Universal Paralinguistic Speech Representations Using Self-Supervised Conformers](https://mlsanity.com/paper/2110.04621/) (2021)
- [Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech](https://mlsanity.com/paper/2109.06952/) (2021)
- [Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2202.10290/) (2022)
- [Widening Access to Applied Machine Learning with TinyML](https://mlsanity.com/paper/2106.04008/) (2021)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech](https://mlsanity.com/paper/2405.06150/) (2024)
- [Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets](https://mlsanity.com/paper/2110.04612/) (2021)
- [Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2302.14564/) (2023)
- [Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition](https://mlsanity.com/paper/2201.05554/) (2022)
- [Accented Speech Recognition: A Survey](https://mlsanity.com/paper/2104.10747/) (2021)
- [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024)
- [An analysis of degenerating speech due to progressive dysarthria on ASR performance](https://mlsanity.com/paper/2211.00089/) (2022)
- [On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech](https://mlsanity.com/paper/2106.10259/) (2021)
- [Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases](https://mlsanity.com/paper/2107.03985/) (2021)
- [On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2203.14593/) (2022)
- [Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition](https://mlsanity.com/paper/2306.15265/) (2023)
- [Replay to Remember: Continual Layer-Specific Fine-tuning for German Speech Recognition](https://mlsanity.com/paper/2307.07280/) (2023)
- [Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition](https://mlsanity.com/paper/2406.09873/) (2024)
- [Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation](https://mlsanity.com/paper/2407.18461/) (2024)
- [Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech](https://mlsanity.com/paper/2506.21622/) (2025)
- [Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition](https://mlsanity.com/paper/2509.20397/) (2025)
- [Multi-pass Training and Cross-information Fusion for Low-resource End-to-end Accented Speech Recognition](https://mlsanity.com/paper/2306.11309/) (2023)
- [Error-driven Fixed-Budget ASR Personalization for Accented Speakers](https://mlsanity.com/paper/2103.03142/) (2021)
- [Exploring and Evaluating Personalized Models for Code Generation](https://mlsanity.com/paper/2208.13928/) (2022)
- [Latent Phrase Matching for Dysarthric Speech](https://mlsanity.com/paper/2306.05446/) (2023)
- [Hypernetworks for Personalizing ASR to Atypical Speech](https://mlsanity.com/paper/2406.04240/) (2024)
- [Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation](https://mlsanity.com/paper/2407.06310/) (2024)
- [The Sound of Syntax: Finetuning and Comprehensive Evaluation of Language Models for Speech Pathology](https://mlsanity.com/paper/2509.16765/) (2025)
- [Improving Voice Trigger Detection with Metric Learning](https://mlsanity.com/paper/2204.02455/) (2022)
- [Large Language Model Data Generation for Enhanced Intent Recognition in German Speech](https://mlsanity.com/paper/2508.06277/) (2025)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from 'typical' speech, which means that underrepresented groups don't experience the same level of improvement. In this paper, we present and evaluate finetuning techniques to improve ASR for users with non-standard speech. We focus on two types of non-standard speech: speech from people with amyotrophic lateral sclerosis (ALS) and accented speech. We train personalized models that achieve 62% and 35% relative WER improvement on these two groups, bringing the absolute WER for ALS speakers, on a test set of message bank phrases, down to 10% for mild dysarthria and 20% for more serious dysarthria. We show that 71% of the improvement comes from only 5 minutes of training data. Finetuning a particular subset of layers (with many fewer parameters) often gives better results than finetuning the entire model. This is the first step towards building state of the art ASR models for dysarthric speech.