2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) 2017
DOI: 10.1109/itcosp.2017.8303086
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Cited by 9 publications
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“…With the emergence of deep learning algorithms, pathological speech classification models based on neural networks have also been proposed. For example, in [4], [5], [6] and [7], MFCCs serve as input vectors to a Multi Layer Perceptron (MLP). MLP drawbacks however, include overfitting and a potentially long training period due to the large number of model parameters.…”
Section: Fig 1 a Typical Pathological Speech Classification Modelmentioning
confidence: 99%
“…With the emergence of deep learning algorithms, pathological speech classification models based on neural networks have also been proposed. For example, in [4], [5], [6] and [7], MFCCs serve as input vectors to a Multi Layer Perceptron (MLP). MLP drawbacks however, include overfitting and a potentially long training period due to the large number of model parameters.…”
Section: Fig 1 a Typical Pathological Speech Classification Modelmentioning
confidence: 99%
“…Shia and Jayasree [9] presented a voice pathology detection system using the Discrete Wavelet Transform method (DWT) and Feed Forward Neural Network (FFNN). They used the Saarbruecken Voice Database (SVD) to test their work.…”
Section: Introductionmentioning
confidence: 99%
“…They chose only a few diseases [1], [7], [10] such as dysphonia, polyps, cysts, or paralysis. Moreover, the number of the voice signals used in the test phase is too small [9]- [12].…”
Section: Introductionmentioning
confidence: 99%