# On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition

arXiv: https://arxiv.org/abs/2203.14593 · HTML: https://mlsanity.com/paper/2203.14593/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://mlsanity.com/paper/1601.02828/) (2016)
- [Investigation of Data Augmentation Techniques for Disordered Speech Recognition](https://mlsanity.com/paper/2201.05562/) (2022)
- [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)
- [Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognition](https://mlsanity.com/paper/2201.05554/) (2022)
- [Bayesian Learning for Deep Neural Network Adaptation](https://mlsanity.com/paper/2012.07460/) (2020)
- [Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition](https://mlsanity.com/paper/2203.10274/) (2022)
- [Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition](https://mlsanity.com/paper/2012.04494/) (2020)

## Built on by (most cited first, 5 of 5)

- [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024)
- [Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition](https://mlsanity.com/paper/2406.09873/) (2024)
- [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)
- [Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Study](https://mlsanity.com/paper/2608.01865/) (2026)

## Abstract (arXiv)

Accurate recognition of dysarthric and elderly speech remain challenging tasks to date. Speaker-level heterogeneity attributed to accent or gender, when aggregated with age and speech impairment, create large diversity among these speakers. Scarcity of speaker-level data limits the practical use of data-intensive model based speaker adaptation methods. To this end, this paper proposes two novel forms of data-efficient, feature-based on-the-fly speaker adaptation methods: variance-regularized spectral basis embedding (SVR) and spectral feature driven f-LHUC transforms. Experiments conducted on UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest the proposed on-the-fly speaker adaptation approaches consistently outperform baseline iVector adapted hybrid DNN/TDNN and E2E Conformer systems by statistically significant WER reduction of 2.48%-2.85% absolute (7.92%-8.06% relative), and offline model based LHUC adaptation by 1.82% absolute (5.63% relative) respectively.