# Augmenting Dysarthric Speech Severity Assessment with MOS Supervision

arXiv: https://arxiv.org/abs/2606.18645 · HTML: https://mlsanity.com/paper/2606.18645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 51 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 30 of 37)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets](https://mlsanity.com/paper/2104.09494/) (2021)
- [MOSNet: Deep Learning based Objective Assessment for Voice Conversion](https://mlsanity.com/paper/1904.08352/) (2019)
- [Zipformer: A faster and better encoder for automatic speech recognition](https://mlsanity.com/paper/2310.11230/) (2023)
- [Generalization Ability of MOS Prediction Networks](https://mlsanity.com/paper/2110.02635/) (2021)
- [The VoiceMOS Challenge 2022](https://mlsanity.com/paper/2203.11389/) (2022)
- [Investigation of Data Augmentation Techniques for Disordered Speech Recognition](https://mlsanity.com/paper/2201.05562/) (2022)
- [Audio Large Language Models Can Be Descriptive Speech Quality Evaluators](https://mlsanity.com/paper/2501.17202/) (2025)
- [How do Voices from Past Speech Synthesis Challenges Compare Today?](https://mlsanity.com/paper/2105.02373/) (2021)
- [Adversarial Data Augmentation for Disordered Speech Recognition](https://mlsanity.com/paper/2108.00899/) (2021)
- [Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation](https://mlsanity.com/paper/2401.00662/) (2024)
- [CR-CTC: Consistency regularization on CTC for improved speech recognition](https://mlsanity.com/paper/2410.05101/) (2024)
- [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022)
- [Wav2vec-based Detection and Severity Level Classification of Dysarthria from Speech](https://mlsanity.com/paper/2309.14107/) (2023)
- [Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis](https://mlsanity.com/paper/2406.08568/) (2024)
- [Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation](https://mlsanity.com/paper/2409.16644/) (2024)
- [QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions](https://mlsanity.com/paper/2503.20290/) (2025)
- [The Interspeech 2025 Speech Accessibility Project Challenge](https://mlsanity.com/paper/2507.22047/) (2025)
- [Use of Speech Impairment Severity for Dysarthric Speech Recognition](https://mlsanity.com/paper/2305.10659/) (2023)
- [UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization](https://mlsanity.com/paper/2401.14664/) (2024)
- [Adversarial Data Augmentation Using VAE-GAN for Disordered Speech Recognition](https://mlsanity.com/paper/2211.01646/) (2022)
- [Measuring Prosody Diversity in Zero-Shot TTS: A New Metric, Benchmark, and Exploration](https://mlsanity.com/paper/2509.19928/) (2025)
- [Automatic Severity Classification of Dysarthric speech by using Self-supervised Model with Multi-task Learning](https://mlsanity.com/paper/2210.15387/) (2022)
- [DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model](https://mlsanity.com/paper/2306.10588/) (2023)

## Abstract (arXiv)

Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness. Automatic utterance-level assessment of dysarthric speech can support scalable speech monitoring and therapy-related analysis. Yet training such systems is bottlenecked by the scarcity of clinically annotated dysarthric speech. This work proposes to augment dysarthric speech assessment using data from speech synthesis evaluations, specifically human-annotated utterances with Mean Opinion Score (MOS) labels from the QualiSpeech corpus. Experiments show that fine-tuning on speech synthesis assessment data consistently improves performance on both intelligibility and naturalness prediction, while joint training yields gains primarily on naturalness. These results suggest that synthesis artifacts and dysarthric speech share perceptual commonalities, and speech synthesis evaluation corpora offer a practical augmentation source that reduces reliance on scarce clinical annotations.