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

## TL;DR

This paper presents a cost-effective and efficient machine learning approach using Mel-Cepstrum vectors and Support Vector Machine to classify vocal disorders, aiming to improve diagnosis accuracy and accessibility.

## Contribution

It introduces a novel application of SVM with Mel-Cepstrum features for vocal disorder classification, addressing diagnostic challenges without expensive equipment.

## Key findings

- High classification accuracy achieved
- Reduced diagnostic costs
- Effective for multiple vocal disorders

## Abstract

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.