mlsanityData to 7 Oct 2026

Speech Intelligibility Assessment of Dysarthric Speech by using Goodness of Pronunciation with Uncertainty Quantification

arXiv 2305.18392 ↗First posted 28 May 2023cs.SD cs.LG eess.AScited by 10

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Abstract (arXiv)

This paper proposes an improved Goodness of Pronunciation (GoP) that utilizes Uncertainty Quantification (UQ) for automatic speech intelligibility assessment for dysarthric speech. Current GoP methods rely heavily on neural network-driven overconfident predictions, which is unsuitable for assessing dysarthric speech due to its significant acoustic differences from healthy speech. To alleviate the problem, UQ techniques were used on GoP by 1) normalizing the phoneme prediction (entropy, margin, maxlogit, logit-margin) and 2) modifying the scoring function (scaling, prior normalization). As a result, prior-normalized maxlogit GoP achieves the best performance, with a relative increase of 5.66%, 3.91%, and 23.65% compared to the baseline GoP for English, Korean, and Tamil, respectively. Furthermore, phoneme analysis is conducted to identify which phoneme scores significantly correlate with intelligibility scores in each language.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 28 May 2023.

Builds on

11 of 41 references.

References · 11

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  10. 2022Multilingual analysis of intelligibility classification using English, Korean, and Tamil dysarthric speech datasetscited by 2
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Cited by year (2026 to 7 Oct)

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

10 papers.

  1. 2025Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessmentcited by 15
  2. 2026Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modelingcited by 4
  3. 2025Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languagescited by 3
  4. 2025Evaluating Logit-Based GOP Scores for Mispronunciation Detectioncited by 1
  5. 2024A Comprehensive Rubric for Annotating Pathological Speechcited by 0
  6. 2024Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based modelscited by 0
  7. 2025Bridging the Perceptual-Statistical Gap in Dysarthria Assessment: Why Machine Learning Still Falls Shortcited by 0
  8. 2026Phone Segmentation and Recognition through Phonological Activation Mappingcited by 0
  9. 2026Quantifying Consonant Contributions to Word Intelligibility via Acoustic Maskingcited by 0
  10. 2026Automatic estimation of verbal fluency index in people with Motor Neuron Disease using ASR alignment and pause modellingcited by 0