Voice pathology detection by using the deep network architecture

Pathological voice disorders are among the conditions affecting negatively our daily life. The aim of this study is to introduce the new feature vector in the hybrid axis and multi-model in order to diagnose these disorders with more conventional methods. Two different databases are used, and the results are compared with the previous studies. Here, two types of fusion models (feature and decision level fusion) are used to increase the classification accuracy of the multi-model. The experimental results show that the proposed multi-model gives the highest classification accuracies with decision level fusion (DLF). Inspecting the results obtained from two databases, the highest accuracy rate (99.58%) is obtained with DLF. It is also seen from the experiments that the proposed feature vector helps to classify pathological data successfully, depending on their pathological conditions. Together with the proposed multi-model, both LSTM and CNN are found to be similarly successful in the classification of data in multi-model architecture. (C) 2021 Elsevier B.V. All rights reserved.