Neural networks and their learning algorithms

The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotions: 0 – neutral, 1 – calm, 2 – happiness, 3 – sadness, 4 – anger, 5 – fear, 6 – disgust, 7 – surprise. In total, the database contains 16 classes (8 emotions divided into male and female) for a total of 1440 samples (speech only). To train machine learning algorithms and deep neural networks to recognize emotions, existing audio recordings must be pre-processed in such a way as to extract the main characteristic features of certain emotions. This was done using Mel-frequency cepstral coefficients, chroma coefficients, as well as the characteristics of the frequency spectrum of audio recordings. In this paper, computer studies of various models of neural networks for emotion recognition are carried out on the example of the data described above. In addition, machine learning algorithms were used for comparative analysis. Thus, the following models were trained during the experiments: logistic regression (LR), classifier based on the support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting over trees – XGBoost, convolutional neural network CNN, recurrent neural network RNN (ResNet18), as well as an ensemble of convolutional and recurrent networks Stacked CNN-RNN. The results show that neural networks showed much higher accuracy in recognizing and classifying emotions than the machine learning algorithms used. Of the three neural network models presented, the CNN + BLSTM ensemble showed higher accuracy.

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ELEANNE: Efficient LEarning Algorithms for Neural NEtworks

Artificial Neural Networks ◽

10.1007/978-1-4757-4547-4_3 ◽

1993 ◽

pp. 87-139

Author(s):

N. B. Karayiannis ◽

A. N. Venetsanopoulos

Keyword(s):

Neural Networks ◽

Learning Algorithms ◽

Efficient Learning

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Diagnosis of Malignancy in Thyroid Tumors by Multi-Layer Perceptron Neural Networks With Different Batch Learning Algorithms

Global Journal of Health Science ◽

10.5539/gjhs.v7n6p46 ◽

2015 ◽

Vol 7 (6) ◽

Cited By ~ 5

Author(s):

Saeedeh Pourahmad ◽

Mohsen Azad ◽

Shahram Paydar

Keyword(s):

Neural Networks ◽

Learning Algorithms ◽

Thyroid Tumors ◽

Multi Layer Perceptron ◽

Batch Learning

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Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

The Open Neurology Journal ◽

10.2174/1874205x01408010015 ◽

2014 ◽

Vol 8 (1) ◽

pp. 15-21

Author(s):

Dmitrienko V. D ◽

Yu. Zakovorotnyi A ◽

Yu. Leonov S ◽

Khavina I. P

Keyword(s):

Neural Networks ◽

Multiple Solutions ◽

Input Data ◽

Learning Algorithms ◽

Adaptive Resonance Theory ◽

Classification Methods ◽

Resonance Theory ◽

Adaptive Resonance ◽

Noisy Input ◽

New Algorithms

A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.

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