… voice feature alteration in COVID-19 patients after the recent resolution of disease using deep
learning. … In conclusion, deep learning is able to detect the subtle change in voice features …

… In this work, we experimented on two different approaches to voice classification. The
data we worked with is variable length. Traditionally recurrent neural network is a preferred …

… Proposed approach: Deep learning of pathologically enhanced speech In this section,
the proposed voice pathology classification system is presented. As illustrated by the block …

… robust voice pathology detection and classification … learning techniques, a new architecture
is proposed herein which initially performs deep learning-based filtering of the input voice …

… Gürhanlı, “Voice disorder classification based on multitaper mel frequency cepstral coefficients
features,” Computational and mathematical methods in medicine, vol. 2015, 2015. [13] A. …

… -propagation learning algorithm to … in classification with this deep learning approach. A
performance analysis of the novel method is provided using real data from the Saarbrucken Voice …

… In this paper we have employed deep learning techniques to build neural networks to solve
the singer vocal classification problem. We have proposed a method to solve this problem …

… raw audio files instead of applying image augmentation techniques to the extracted feature
matrixes in order to improve the performance of voice classification deep learning models. …

… for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based
approach to detect pathological voice and … with other automatic classification algorithms. …

… , deep learning architectures were not investigated in the context of automatic feature learning
… disorders which are much easily distinguishable from healthy person voice. Contribution: …