# Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine

arXiv: https://arxiv.org/abs/1812.07729 · HTML: https://mlsanity.com/paper/1812.07729/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Continuous Speech for Improved Learning Pathological Voice Disorders](https://mlsanity.com/paper/2202.10777/) (2022)
- [Detecting Throat Cancer from Speech Signals using Machine Learning: A Scoping Literature Review](https://mlsanity.com/paper/2307.09230/) (2023)

## Abstract (arXiv)

Vocal disorders have affected several patients all over the world. Due to the inherent difficulty of diagnosing vocal disorders without sophisticated equipment and trained personnel, a number of patients remain undiagnosed. To alleviate the monetary cost of diagnosis, there has been a recent growth in the use of data analysis to accurately detect and diagnose individuals for a fraction of the cost. We propose a cheap, efficient and accurate model to diagnose whether a patient suffers from one of three vocal disorders on the FEMH 2018 challenge.