# Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition

arXiv: https://arxiv.org/abs/2202.10290 · HTML: https://mlsanity.com/paper/2202.10290/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 108 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Transformer-based Acoustic Modeling for Hybrid Speech Recognition](https://mlsanity.com/paper/1910.09799/) (2019)
- [A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation](https://mlsanity.com/paper/2010.14766/) (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)
- [On the limit of English conversational speech recognition](https://mlsanity.com/paper/2105.00982/) (2021)
- [Investigation of Data Augmentation Techniques for Disordered Speech Recognition](https://mlsanity.com/paper/2201.05562/) (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)
- [Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks](https://mlsanity.com/paper/2201.03943/) (2022)
- [Variational Auto-Encoder Based Variability Encoding for Dysarthric Speech Recognition](https://mlsanity.com/paper/2201.09422/) (2022)
- [Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognition](https://mlsanity.com/paper/2012.04494/) (2020)
- [A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech](https://mlsanity.com/paper/2106.08313/) (2021)
- [Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation](https://mlsanity.com/paper/1511.05076/) (2015)

## Built on by (most cited first, 19 of 19)

- [Speaker adaptation for Wav2vec2 based dysarthric ASR](https://mlsanity.com/paper/2204.00770/) (2022)
- [Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2302.14564/) (2023)
- [Exploring linguistic feature and model combination for speech recognition based automatic AD detection](https://mlsanity.com/paper/2206.13758/) (2022)
- [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)
- [Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition](https://mlsanity.com/paper/2206.07327/) (2022)
- [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)
- [Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test](https://mlsanity.com/paper/2305.13108/) (2023)
- [SeniorTalk: A Chinese Conversation Dataset with Rich Annotations for Super-Aged Seniors](https://mlsanity.com/paper/2503.16578/) (2025)
- [Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with Aphasia](https://mlsanity.com/paper/2506.08846/) (2025)
- [Learning Separable Hidden Unit Contributions for Speaker-Adaptive Lip-Reading](https://mlsanity.com/paper/2310.05058/) (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)
- [Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2506.11069/) (2025)
- [Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation](https://mlsanity.com/paper/2306.04368/) (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)
- [Elderly-Contextual Data Augmentation via Speech Synthesis for Elderly ASR](https://mlsanity.com/paper/2604.24770/) (2026)
- [Towards Personalized Federated Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2606.13253/) (2026)

## Abstract (arXiv)

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech in recent decades, accurate recognition of dysarthric and elderly speech remains highly challenging tasks to date. Sources of heterogeneity commonly found in normal speech including accent or gender, when further compounded with the variability over age and speech pathology severity level, create large diversity among speakers. To this end, speaker adaptation techniques play a key role in personalization of ASR systems for such users. Motivated by the spectro-temporal level differences between dysarthric, elderly and normal speech that systematically manifest in articulatory imprecision, decreased volume and clarity, slower speaking rates and increased dysfluencies, novel spectrotemporal subspace basis deep embedding features derived using SVD speech spectrum decomposition are proposed in this paper to facilitate auxiliary feature based speaker adaptation of state-of-the-art hybrid DNN/TDNN and end-to-end Conformer speech recognition systems. Experiments were conducted on four tasks: the English UASpeech and TORGO dysarthric speech corpora; the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets. The proposed spectro-temporal deep feature adapted systems outperformed baseline i-Vector and xVector adaptation by up to 2.63% absolute (8.63% relative) reduction in word error rate (WER). Consistent performance improvements were retained after model based speaker adaptation using learning hidden unit contributions (LHUC) was further applied. The best speaker adapted system using the proposed spectral basis embedding features produced the lowest published WER of 25.05% on the UASpeech test set of 16 dysarthric speakers.