Augmenting Dysarthric Speech Severity Assessment with MOS Supervision

• URL: http://arxiv.org/abs/2606.18645v1
• Date: Wed, 17 Jun 2026 03:29:40 GMT
• Title: Augmenting Dysarthric Speech Severity Assessment with MOS Supervision
• Abstract summary: This work proposes to augment dysarthric speech assessment using data from speech synthesis evaluations.<n>Experiments show that fine-tuning on speech synthesis assessment data consistently improves performance on both intelligibility and naturalness prediction.
• Score: 13.782589618988501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Synthesizing the Lombard Effect: Multi-Level Control of Speech Clarity and Vocal Effort in TTS [61.29502937013759]
  We introduce a flow-matching based text-to-speech model trained with vocal effort and articulation pseudo-labels.<n>The proposed model achieves continuous and disentangled control of vocal effort and articulation, while also enabling word-level emphasis for clarifying specific segments of an utterance.
  arXiv  Detail & Related papers  (2026-06-22T11:14:22Z)
• On the Emotion Understanding of Synthesized Speech [63.13411068766772]
  Emotion is a core paralinguistic feature in voice interaction.<n>Current Speech Emotion Recognition (SER) models can not generalize to synthesized speech.<n>generative Speech Language Models (SLMs) tend to infer emotion from textual semantics while ignoring paralinguistic cues.
  arXiv  Detail & Related papers  (2026-03-17T13:11:14Z)
• Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS [10.019926246026928]
  Dysarthric speech poses significant challenges in developing assistive technologies.<n>Recent advances in neural speech synthesis, especially zero-shot voice cloning, facilitate synthetic speech generation for data augmentation.<n>We investigate the effectiveness of state-of-the-art F5-TTS in cloning dysarthric speech using TORGO dataset.
  arXiv  Detail & Related papers  (2025-08-07T07:39:48Z)
• Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching [0.0]
  Dysarthria is a neurological disorder that significantly impairs speech intelligibility.<n>This necessitates the development of robust dysarthric-to-regular speech conversion techniques.
  arXiv  Detail & Related papers  (2025-06-19T08:24:17Z)
• Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages [49.31519786009296]
  We fine-tune a voice conversion model on English dysarthric speech (UASpeech) to encode both speaker characteristics and prosodic distortions.<n>We then apply it to convert healthy non-English speech (FLEURS) into non-English dysarthric-like speech.<n>The generated data is then used to fine-tune a multilingual ASR model, Massively Multilingually Speech (MMS)
  arXiv  Detail & Related papers  (2025-05-20T20:03:45Z)
• Evaluating Speech Synthesis by Training Recognizers on Synthetic Speech [34.8899247119748]
  We propose an evaluation technique involving the training of an ASR model on synthetic speech and assessing its performance on real speech. Our proposed metric demonstrates a strong correlation with both MOS naturalness and MOS intelligibility when compared to SpeechLMScore and MOSNet.
  arXiv  Detail & Related papers  (2023-10-01T15:52:48Z)
• Accurate synthesis of Dysarthric Speech for ASR data augmentation [5.223856537504927]
  Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility. This paper presents a new dysarthric speech synthesis method for the purpose of ASR training data augmentation.
  arXiv  Detail & Related papers  (2023-08-16T15:42:24Z)
• EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis [49.04496602282718]
  We introduce Expresso, a high-quality expressive speech dataset for textless speech synthesis. This dataset includes both read speech and improvised dialogues rendered in 26 spontaneous expressive styles. We evaluate resynthesis quality with automatic metrics for different self-supervised discrete encoders.
  arXiv  Detail & Related papers  (2023-08-10T17:41:19Z)
• Investigation of Data Augmentation Techniques for Disordered Speech Recognition [69.50670302435174]
  This paper investigates a set of data augmentation techniques for disordered speech recognition. Both normal and disordered speech were exploited in the augmentation process. The final speaker adapted system constructed using the UASpeech corpus and the best augmentation approach based on speed perturbation produced up to 2.92% absolute word error rate (WER)
  arXiv  Detail & Related papers  (2022-01-14T17:09:22Z)
• Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem [65.25725367771075]
  This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized.
  arXiv  Detail & Related papers  (2021-12-17T08:35:40Z)
• A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion [50.040466658605524]
  We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC) The poor quality of dysarthric speech can be greatly improved by statistical VC. But as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient.
  arXiv  Detail & Related papers  (2021-06-02T18:41:03Z)
• Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training [4.050982413149992]
  The goal of our work is to develop a model for dysarthric to healthy speech conversion using Cycle-consistent GAN. The generator is trained to transform dysarthric to healthy speech in the spectral domain, which is then converted back to speech.
  arXiv  Detail & Related papers  (2020-01-10T01:40:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.