mlsanityData to 14 Oct 2026

Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages

arXiv 2505.14874 ↗First posted 20 May 2025cs.CL cs.SD eess.AScited by 3

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Abstract (arXiv)

Automatic speech recognition (ASR) for dysarthric speech remains challenging due to data scarcity, particularly in non-English languages. To address this, we fine-tune a voice conversion model on English dysarthric speech (UASpeech) to encode both speaker characteristics and prosodic distortions, then apply it to convert healthy non-English speech (FLEURS) into non-English dysarthric-like speech. The generated data is then used to fine-tune a multilingual ASR model, Massively Multilingual Speech (MMS), for improved dysarthric speech recognition. Evaluation on PC-GITA (Spanish), EasyCall (Italian), and SSNCE (Tamil) demonstrates that VC with both speaker and prosody conversion significantly outperforms the off-the-shelf MMS performance and conventional augmentation techniques such as speed and tempo perturbation. Objective and subjective analyses of the generated data further confirm that the generated speech simulates dysarthric characteristics.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 40 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

16 of 40 references.

References · 16

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 649
  3. 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
  4. 2023Scaling Speech Technology to 1,000+ Languagescited by 411
  5. 2024OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identificationcited by 37
  6. 2022Investigation of Data Augmentation Techniques for Disordered Speech Recognitioncited by 27
  7. 2024Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentationcited by 17
  8. 2021EasyCall corpus: a dysarthric speech datasetcited by 15
  9. 2025Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessmentcited by 15
  10. 2018Simulating dysarthric speech for training data augmentation in clinical speech applicationscited by 14
  11. 2023Exploring data augmentation in bias mitigation against non-native-accented speechcited by 11
  12. 2023Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantificationcited by 10
4 more
  1. 2023Accurate synthesis of Dysarthric Speech for ASR data augmentationcited by 7
  2. 2024Improving child speech recognition with augmented child-like speechcited by 7
  3. 2022A unified one-shot prosody and speaker conversion system with self-supervised discrete speech unitscited by 3
  4. 2021Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognitioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026The Voice of Equity: A Systematic Evaluation of Bias Mitigation Techniques for Speech-Based Cognitive Impairment Detection Across Architectures and Demographicscited by 2
  2. 2025Confidence-Guided Error Correction for Disordered Speech Recognitioncited by 1
  3. 2026Adapting Foundation ASR Models to Dysarthric Speech: A Case Studycited by 1