# A Classification Benchmark for Artificial Intelligence Detection of Laryngeal Cancer from Patient Voice

arXiv: https://arxiv.org/abs/2412.16267 · HTML: https://mlsanity.com/paper/2412.16267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [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)
- [Multi-class Detection of Pathological Speech with Latent Features: How does it perform on unseen data?](https://mlsanity.com/paper/2210.15336/) (2022)
- [Parameterization of Sequence of MFCCs for DNN-based voice disorder detection](https://mlsanity.com/paper/1812.05888/) (2018)
- [Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review](https://mlsanity.com/paper/2307.09230/) (2023)

## Abstract (arXiv)

Cases of laryngeal cancer are predicted to rise significantly in the coming years. Current diagnostic pathways are inefficient, putting undue stress on both patients and the medical system. Artificial intelligence offers a promising solution by enabling non-invasive detection of laryngeal cancer from patient voice, which could help prioritise referrals more effectively. A major barrier in this field is the lack of reproducible methods. Our work addresses this challenge by introducing a benchmark suite comprising 36 models trained and evaluated on open-source datasets. These models classify patients with benign and malignant voice pathologies. All models are accessible in a public repository, providing a foundation for future research. We evaluate three algorithms and three audio feature sets, including both audio-only inputs and multimodal inputs incorporating demographic and symptom data. Our best model achieves a balanced accuracy of 83.7%, sensitivity of 84.0%, specificity of 83.3%, and AUROC of 91.8%.