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

arXiv: https://arxiv.org/abs/2305.18392 · HTML: https://mlsanity.com/paper/2305.18392/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 11 of 11)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Scaling Out-of-Distribution Detection for Real-World Settings](https://mlsanity.com/paper/1911.11132/) (2019)
- [Disentangling Label Distribution for Long-tailed Visual Recognition](https://mlsanity.com/paper/2012.00321/) (2020)
- [Opening the Black Box of wav2vec Feature Encoder](https://mlsanity.com/paper/2210.15386/) (2022)
- [ASR-Free Pronunciation Assessment](https://mlsanity.com/paper/2005.11902/) (2020)
- [Common Phone: A Multilingual Dataset for Robust Acoustic Modelling](https://mlsanity.com/paper/2201.05912/) (2022)
- [Multilingual analysis of intelligibility classification using English, Korean, and Tamil dysarthric speech datasets](https://mlsanity.com/paper/2209.13260/) (2022)
- [Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment](https://mlsanity.com/paper/2108.06943/) (2021)

## Built on by (most cited first, 10 of 10)

- [Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment](https://mlsanity.com/paper/2502.07029/) (2025)
- [Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling](https://mlsanity.com/paper/2601.21205/) (2026)
- [Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages](https://mlsanity.com/paper/2505.14874/) (2025)
- [Evaluating Logit-Based GOP Scores for Mispronunciation Detection](https://mlsanity.com/paper/2506.12067/) (2025)
- [A Comprehensive Rubric for Annotating Pathological Speech](https://mlsanity.com/paper/2404.18851/) (2024)
- [Towards objective and interpretable speech disorder assessment: a comparative analysis of CNN and transformer-based models](https://mlsanity.com/paper/2406.07576/) (2024)
- [Bridging the Perceptual-Statistical Gap in Dysarthria Assessment: Why Machine Learning Still Falls Short](https://mlsanity.com/paper/2510.22237/) (2025)
- [Phone Segmentation and Recognition through Phonological Activation Mapping](https://mlsanity.com/paper/2607.09020/) (2026)
- [Quantifying Consonant Contributions to Word Intelligibility via Acoustic Masking](https://mlsanity.com/paper/2609.12122/) (2026)
- [Automatic estimation of verbal fluency index in people with Motor Neuron Disease using ASR alignment and pause modelling](https://mlsanity.com/paper/2609.38203/) (2026)

## 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.