A Novel Neural Network-Based Approach to Classification of Implicit Emotional Components in Ordinary Speech
| Autor: | V. N. Kiroy, D. G. Shaposhnikov, O. M. Bakhtin, Anton Igorevich Saevskiy, I. E. Shepelev, D. M. Lazurenko |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Artificial neural network business.industry Computer science Feature vector Sample (statistics) Pattern recognition 02 engineering and technology 01 natural sciences Speech patterns Electronic Optical and Magnetic Materials 010309 optics Multilayer perceptron 0103 physical sciences Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Hidden layer Mel-frequency cepstrum Electrical and Electronic Engineering business |
| Zdroj: | Optical Memory and Neural Networks. 30:26-36 |
| ISSN: | 1934-7898 1060-992X |
| DOI: | 10.3103/s1060992x21010057 |
| Popis: | The neural network-based approach to the classification of implicit emotional components in ordinary speech is considered. Mel-frequency cepstral coefficients were used as feature vectors, and the multilayer perceptron with one hidden layer was used as the classifier. It was shown that the neural-network system developed is able to classify these kinds of speech with the accuracy up to 99% and is not inferior to the human experts. Moreover, two model’s training approaches were suggested and tested, and the influence of the parameters for mel-frequency cepstral coefficients calculation on the resulting accuracies was studied. It was found that the personalized approach to training the classifier for each subject results in higher classification accuracy than the generalized one that is, using a mixed sample of multiple subjects. Optimal parameters for the mel-frequency cepstral coefficients calculations were found. The results of the study demonstrated high quality of the developed approach, and it can be applied to developing Brain-Computer interfaces based on inner speech patterns recognition, which will be addressed in further research. |
| Databáze: | OpenAIRE |
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