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

2018/12/18 by Maryam Pishgar, Pishgar, Maryam, Fazle Karim +5
Computer Science · Medicine · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #Voice and Speech Disorders #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1812.07729

openalex publication_date 2018/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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