mlsanityData to 14 Oct 2026

Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech

arXiv 2309.14107 ↗First posted 25 Sep 2023eess.AS cs.CL cs.LGcited by 14

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Abstract (arXiv)

Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dysarthric speech. The experiments were carried out with the popularly used UA-speech database. In the detection experiments, the results revealed that the best performance was obtained using the embeddings from the first layer of the wav2vec model that yielded an absolute improvement of 1.23% in accuracy compared to the best performing baseline feature (spectrogram). In the studied severity level classification task, the results revealed that the embeddings from the final layer gave an absolute improvement of 10.62% in accuracy compared to the best baseline features (mel-frequency cepstral coefficients).

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

4 of 23 references.

References · 4

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2021Fine-tuning wav2vec2 for speaker recognitioncited by 39
  3. 2022Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognitioncited by 17
  4. 2021Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED Datasetcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 2; 2026: 4.

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  1. 2024Self-supervised Speech Models for Word-Level Stuttered Speech Detectioncited by 4
  2. 2024Impact of Speech Mode in Automatic Pathological Speech Detectioncited by 2
  3. 2024WavRx: a Disease-Agnostic, Generalizable, and Privacy-Preserving Speech Health Diagnostic Modelcited by 2
  4. 2024Graph Neural Networks for Parkinsons Disease Detectioncited by 1
  5. 2024Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detectioncited by 1
  6. 2024A Multi-modal Approach to Dysarthria Detection and Severity Assessment Using Speech and Text Informationcited by 1
  7. 2025Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studiescited by 1
  8. 2023Classification of Dysarthria based on the Levels of Severity. A Systematic Reviewcited by 0
  9. 2024Selfsupervised learning for pathological speech detectioncited by 0
  10. 2025Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignalscited by 0
  11. 2026Augmenting Dysarthric Speech Severity Assessment with MOS Supervisioncited by 0
  12. 2026Improving End-to-End Speech Recognition for Dysarthric Speech through In-Domain Data Augmentationcited by 0
2 more
  1. 2026Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessmentcited by 0
  2. 2026What Does a Pathological Speech Assessment Model Know about Acoustic Features? A Case Study on Oral and Oropharyngeal Cancer Patientscited by 0