# Pathological voice adaptation with autoencoder-based voice conversion

arXiv: https://arxiv.org/abs/2106.08427 · HTML: https://mlsanity.com/paper/2106.08427/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron](https://mlsanity.com/paper/1803.09047/) (2018)
- [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020)
- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion](https://mlsanity.com/paper/1904.04631/) (2019)
- [Learning Latent Representations for Speech Generation and Transformation](https://mlsanity.com/paper/1704.04222/) (2017)
- [CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectrogram Conversion](https://mlsanity.com/paper/2010.11672/) (2020)
- [Simulating dysarthric speech for training data augmentation in clinical speech applications](https://mlsanity.com/paper/1804.10325/) (2018)

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

- [Towards Identity Preserving Normal to Dysarthric Voice Conversion](https://mlsanity.com/paper/2110.08213/) (2021)
- [DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model](https://mlsanity.com/paper/2306.10588/) (2023)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [The Voice of Equity: A Systematic Evaluation of Bias Mitigation Techniques for Speech-Based Cognitive Impairment Detection Across Architectures and Demographics](https://mlsanity.com/paper/2601.16989/) (2026)
- [Manipulation of oral cancer speech using neural articulatory synthesis](https://mlsanity.com/paper/2203.17072/) (2022)

## Abstract (arXiv)

In this paper, we propose a new approach to pathological speech synthesis. Instead of using healthy speech as a source, we customise an existing pathological speech sample to a new speaker's voice characteristics. This approach alleviates the evaluation problem one normally has when converting typical speech to pathological speech, as in our approach, the voice conversion (VC) model does not need to be optimised for speech degradation but only for the speaker change. This change in the optimisation ensures that any degradation found in naturalness is due to the conversion process and not due to the model exaggerating characteristics of a speech pathology. To show a proof of concept of this method, we convert dysarthric speech using the UASpeech database and an autoencoder-based VC technique. Subjective evaluation results show reasonable naturalness for high intelligibility dysarthric speakers, though lower intelligibility seems to introduce a marginal degradation in naturalness scores for mid and low intelligibility speakers compared to ground truth. Conversion of speaker characteristics for low and high intelligibility speakers is successful, but not for mid. Whether the differences in the results for the different intelligibility levels is due to the intelligibility levels or due to the speakers needs to be further investigated.