# Speaker adaptation for Wav2vec2 based dysarthric ASR

arXiv: https://arxiv.org/abs/2204.00770 · HTML: https://mlsanity.com/paper/2204.00770/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 2 Apr 2022.

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

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2202.10290/) (2022)
- [Adversarial Data Augmentation for Disordered Speech Recognition](https://mlsanity.com/paper/2108.00899/) (2021)
- [Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization](https://mlsanity.com/paper/2011.01686/) (2020)
- [Recent Progress in the CUHK Dysarthric Speech Recognition System](https://mlsanity.com/paper/2201.05845/) (2022)
- [Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition](https://mlsanity.com/paper/2201.09422/) (2022)
- [Unsupervised Domain Adaptation for Dysarthric Speech Detection via Domain Adversarial Training and Mutual Information Minimization](https://mlsanity.com/paper/2106.10127/) (2021)

## Built on by (most cited first, 21 of 21)

- [Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2302.14564/) (2023)
- [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024)
- [Use of Speech Impairment Severity for Dysarthric Speech Recognition](https://mlsanity.com/paper/2305.10659/) (2023)
- [Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation](https://mlsanity.com/paper/2406.10177/) (2024)
- [Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition](https://mlsanity.com/paper/2406.09873/) (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)
- [Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test](https://mlsanity.com/paper/2305.13108/) (2023)
- [Latent Phrase Matching for Dysarthric Speech](https://mlsanity.com/paper/2306.05446/) (2023)
- [Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation](https://mlsanity.com/paper/2407.06310/) (2024)
- [DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2501.19010/) (2025)
- [Unsupervised speech intelligibility assessment with utterance level alignment distance between teacher and learner Wav2Vec-2.0 representations](https://mlsanity.com/paper/2306.08845/) (2023)
- [Personalizing Keyword Spotting with Speaker Information](https://mlsanity.com/paper/2311.03419/) (2023)
- [Robust Cross-Etiology and Speaker-Independent Dysarthric Speech Recognition](https://mlsanity.com/paper/2501.14994/) (2025)
- [Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2506.11069/) (2025)
- [Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies](https://mlsanity.com/paper/2509.16718/) (2025)
- [FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition](https://mlsanity.com/paper/2606.06211/) (2026)
- [Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems](https://mlsanity.com/paper/2306.14608/) (2023)
- [On-the-fly Routing for Zero-shot MoE Speaker Adaptation of Speech Foundation Models for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.22072/) (2025)
- [EvolveCaptions: Empowering DHH Users Through Real-Time Collaborative Captioning](https://mlsanity.com/paper/2510.02181/) (2025)
- [Towards Personalized Federated Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2606.13253/) (2026)

## Abstract (arXiv)

Dysarthric speech recognition has posed major challenges due to lack of training data and heavy mismatch in speaker characteristics. Recent ASR systems have benefited from readily available pretrained models such as wav2vec2 to improve the recognition performance. Speaker adaptation using fMLLR and xvectors have provided major gains for dysarthric speech with very little adaptation data. However, integration of wav2vec2 with fMLLR features or xvectors during wav2vec2 finetuning is yet to be explored. In this work, we propose a simple adaptation network for fine-tuning wav2vec2 using fMLLR features. The adaptation network is also flexible to handle other speaker adaptive features such as xvectors. Experimental analysis show steady improvements using our proposed approach across all impairment severity levels and attains 57.72% WER for high severity in UASpeech dataset. We also performed experiments on German dataset to substantiate the consistency of our proposed approach across diverse domains.