A Multi-Representation Ensemble Approach to Classifying Vocal Diseases
Mingxuan Ju, Zhengkai Jiang, Yufan Chen, Soumya Shubhra Ray · 2018
The goal of the IEEE 2018 FEMH Voice Data Challenge was to develop an effective algorithmic approach to classifying voice samples as normal or pathological, and further subdivide the pathological samples into three types. We adopted a multi-representation ensemble approach to the task. We designed a pipeline with three classification stages, where each stage used a combination of supervised, semi-supervised and multiple-instance learners. This approach was able to achieve a sensitivity of 89% and specificity of 76% in classifying normal from pathological samples and an unweighted average recall (UAR) of 60.67% in subclassifying pathological samples into three types.