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A Novel Neural Network-Based Approach to Classification of Implicit Emotional Components in Ordinary Speech

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Abstract

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.

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Funding

This research was supported by the Russian Science Foundation (RSF) in the framework of “Development of stimulus-unrelated brain-computer interface for disabled people rehabilitation” project (no. 20-19-00627, 2020-2022).

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  1. Research Center of Neurotechnology, Southern Federal University, 344006, Rostov-on-Don, Russia

    I. E. Shepelev, O. M. Bakhtin, D. M. Lazurenko, A. I. Saevskiy, D. G. Shaposhnikov & V. N. Kiroy

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  1. I. E. Shepelev
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  2. O. M. Bakhtin
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Shepelev, I.E., Bakhtin, O.M., Lazurenko, D.M. et al. A Novel Neural Network-Based Approach to Classification of Implicit Emotional Components in Ordinary Speech. Opt. Mem. Neural Networks 30, 26–36 (2021). https://doi.org/10.3103/S1060992X21010057

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