mlsanityData to 7 Oct 2026

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

arXiv 2203.14593 ↗First posted 28 Mar 2022eess.AS cs.AI cs.LGcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 55 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

14 of 55 references.

References · 14

  1. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,838
  2. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 554
  3. 2020Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challengecited by 104
  4. 2019Personalizing ASR for Dysarthric and Accented Speech with Limited Datacited by 45
  5. 2016Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptationcited by 38
  6. 2022Investigation of Data Augmentation Techniques for Disordered Speech Recognitioncited by 27
  7. 2022Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognitioncited by 19
  8. 2021Adversarial Data Augmentation for Disordered Speech Recognitioncited by 18
  9. 2020Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initializationcited by 17
  10. 2022Recent Progress in the CUHK Dysarthric Speech Recognition Systemcited by 17
  11. 2022Spectro-Temporal Deep Features for Disordered Speech Assessment and Recognitioncited by 13
  12. 2020Bayesian Learning for Deep Neural Network Adaptationcited by 9
2 more
  1. 2022Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognitioncited by 9
  2. 2020Bayesian Learning of LF-MMI Trained Time Delay Neural Networks for Speech Recognitioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.

  1. 2024Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognitioncited by 9
  2. 2024Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognitioncited by 4
  3. 2024Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptationcited by 2
  4. 2025DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognitioncited by 2
  5. 2026Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Studycited by 0