# The Effectiveness of Time Stretching for Enhancing Dysarthric Speech for Improved Dysarthric Speech Recognition

arXiv: https://arxiv.org/abs/2201.04908 · HTML: https://mlsanity.com/paper/2201.04908/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis](https://mlsanity.com/paper/1910.06711/) (2019)
- [Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separation](https://mlsanity.com/paper/1904.04169/) (2019)
- [MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames](https://mlsanity.com/paper/2102.12841/) (2021)
- [Simulating dysarthric speech for training data augmentation in clinical speech applications](https://mlsanity.com/paper/1804.10325/) (2018)
- [Towards Identity Preserving Normal to Dysarthric Voice Conversion](https://mlsanity.com/paper/2110.08213/) (2021)
- [A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion](https://mlsanity.com/paper/2106.01415/) (2021)

## Built on by (most cited first, 8 of 8)

- [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024)
- [UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization](https://mlsanity.com/paper/2401.14664/) (2024)
- [Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR](https://mlsanity.com/paper/2501.10256/) (2025)
- [Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation](https://mlsanity.com/paper/2509.19231/) (2025)
- [Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing](https://mlsanity.com/paper/2203.15176/) (2022)
- [Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems](https://mlsanity.com/paper/2307.02009/) (2023)
- [DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2501.19010/) (2025)
- [Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations](https://mlsanity.com/paper/2610.03017/) (2026)

## Abstract (arXiv)

In this paper, we investigate several existing and a new state-of-the-art generative adversarial network-based (GAN) voice conversion method for enhancing dysarthric speech for improved dysarthric speech recognition. We compare key components of existing methods as part of a rigorous ablation study to find the most effective solution to improve dysarthric speech recognition. We find that straightforward signal processing methods such as stationary noise removal and vocoder-based time stretching lead to dysarthric speech recognition results comparable to those obtained when using state-of-the-art GAN-based voice conversion methods as measured using a phoneme recognition task. Additionally, our proposed solution of a combination of MaskCycleGAN-VC and time stretched enhancement is able to improve the phoneme recognition results for certain dysarthric speakers compared to our time stretched baseline.