# Voice Disorder Analysis: a Transformer-based Approach

arXiv: https://arxiv.org/abs/2406.14693 · HTML: https://mlsanity.com/paper/2406.14693/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 20 Jun 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model](https://mlsanity.com/paper/2406.04904/) (2024)
- [Benchmarking Representations for Speech, Music, and Acoustic Events](https://mlsanity.com/paper/2405.00934/) (2024)
- [Detecting Dementia from Speech and Transcripts using Transformers](https://mlsanity.com/paper/2110.14769/) (2021)
- [A Voice Disease Detection Method Based on MFCCs and Shallow CNN](https://mlsanity.com/paper/2304.08708/) (2023)

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

- [Exploring Generative Error Correction for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.20163/) (2025)
- [VocalAgent: Large Language Models for Vocal Health Diagnostics with Safety-Aware Evaluation](https://mlsanity.com/paper/2505.13577/) (2025)
- [MVP: Multi-source Voice Pathology detection](https://mlsanity.com/paper/2505.20050/) (2025)
- [Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations](https://mlsanity.com/paper/2505.21356/) (2025)
- [A Concept-based approach to Voice Disorder Detection](https://mlsanity.com/paper/2507.17799/) (2025)

## Abstract (arXiv)

Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both a shortage of pathological voice data, and diversity of the recording types used for the diagnosis. This paper proposes a novel solution that adopts transformers directly working on raw voice signals and addresses data shortage through synthetic data generation and data augmentation. Further, we consider many recording types at the same time, such as sentence reading and sustained vowel emission, by employing a Mixture of Expert ensemble to align the predictions on different data types. The experimental results, obtained on both public and private datasets, show the effectiveness of our solution in the disorder detection and classification tasks and largely improve over existing approaches.