# Can we reconstruct a dysarthric voice with the large speech model Parler TTS?

arXiv: https://arxiv.org/abs/2506.04397 · HTML: https://mlsanity.com/paper/2506.04397/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022](https://mlsanity.com/paper/2204.02152/) (2022)
- [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://mlsanity.com/paper/2402.01912/) (2024)
- [Speaker-Independent Acoustic-to-Articulatory Speech Inversion](https://mlsanity.com/paper/2302.06774/) (2023)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training](https://mlsanity.com/paper/2001.04260/) (2020)
- [Fast, High-Quality and Parameter-Efficient Articulatory Synthesis using Differentiable DSP](https://mlsanity.com/paper/2409.02451/) (2024)
- [Creating Personalized Synthetic Voices from Articulation Impaired Speech Using Augmented Reconstruction Loss](https://mlsanity.com/paper/2401.03816/) (2024)

## Built on by (most cited first, 3 of 3)

- [Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation](https://mlsanity.com/paper/2509.19231/) (2025)
- [Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech](https://mlsanity.com/paper/2603.15988/) (2026)
- [An Evaluation Framework for Text-to-Speech Voice Reconstruction](https://mlsanity.com/paper/2606.21343/) (2026)

## Abstract (arXiv)

Speech disorders can make communication hard or even impossible for those who develop them. Personalised Text-to-Speech is an attractive option as a communication aid. We attempt voice reconstruction using a large speech model, with which we generate an approximation of a dysarthric speaker's voice prior to the onset of their condition. In particular, we investigate whether a state-of-the-art large speech model, Parler TTS, can generate intelligible speech while maintaining speaker identity. We curate a dataset and annotate it with relevant speaker and intelligibility information, and use this to fine-tune the model. Our results show that the model can indeed learn to generate from the distribution of this challenging data, but struggles to control intelligibility and to maintain consistent speaker identity. We propose future directions to improve controllability of this class of model, for the voice reconstruction task.