Pathological Voice Classification Using Mel-Cepstrum Vectors and Support Vector Machine
This work addresses the issue of costly and inaccessible diagnosis for patients with vocal disorders, though it appears incremental as it applies existing methods to a specific dataset.
The authors tackled the problem of diagnosing vocal disorders by proposing a model that uses Mel-Cepstrum vectors and Support Vector Machine to classify pathological voices on the FEMH 2018 challenge, aiming for a cheap and efficient solution.
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.