# Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR

arXiv: https://arxiv.org/abs/2501.10256 · HTML: https://mlsanity.com/paper/2501.10256/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 17 Jan 2025.

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

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Voice Conversion With Just Nearest Neighbors](https://mlsanity.com/paper/2305.18975/) (2023)
- [Careless Whisper: Speech-to-Text Hallucination Harms](https://mlsanity.com/paper/2402.08021/) (2024)
- [Training Data Augmentation for Dysarthric Automatic Speech Recognition by Text-to-Dysarthric-Speech Synthesis](https://mlsanity.com/paper/2406.08568/) (2024)
- [Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition](https://mlsanity.com/paper/2201.11571/) (2022)
- [The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition](https://mlsanity.com/paper/2201.04908/) (2022)
- [Rhythm Modeling for Voice Conversion](https://mlsanity.com/paper/2307.06040/) (2023)
- [An Objective Evaluation Framework for Pathological Speech Synthesis](https://mlsanity.com/paper/2107.00308/) (2021)
- [Voice Conversion for Stuttered Speech, Instruments, Unseen Languages and Textually Described Voices](https://mlsanity.com/paper/2310.08104/) (2023)

## Built on by (most cited first, 5 of 5)

- [Children's Voice Privacy: First Steps And Emerging Challenges](https://mlsanity.com/paper/2506.00100/) (2025)
- [Unveiling Audio Deepfake Origins: A Deep Metric learning And Conformer Network Approach With Ensemble Fusion](https://mlsanity.com/paper/2506.02085/) (2025)
- [LinearVC: Linear transformations of self-supervised features through the lens of voice conversion](https://mlsanity.com/paper/2506.01510/) (2025)
- [Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation](https://mlsanity.com/paper/2509.19231/) (2025)
- [Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech](https://mlsanity.com/paper/2506.01618/) (2025)

## Abstract (arXiv)

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcribed speech data to estimate speaking rates and phoneme durations, which might not be available for unseen speakers. Therefore, we combine unsupervised rhythm and voice conversion methods based on self-supervised speech representations to map dysarthric to typical speech. We evaluate the outputs with a large ASR model pre-trained on healthy speech without further fine-tuning and find that the proposed rhythm conversion especially improves performance for speakers of the Torgo corpus with more severe cases of dysarthria. Code and audio samples are available at https://idiap.github.io/RnV .