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<p>This paper proposes a method of automatic speaker-independent recognition of human psycho-emotional states by analyzing the speech signal based on Deep Learning technology to solve the problems of aviation profiling. For this purpose, an algorithm to classify seven human psycho-emotional states, including anger, joy, fear, surprise, disgust, sadness, and neutral state was developed. The algorithm is based on the use of Mel-frequency cepstral coefficients and Mel spectrograms as informative features of speech signals audio recordings. These informative features are used to train two deep convolutional neural networks on the generated dataset. The developed classifier testing on a delayed verification dataset showed that the metric for the multiclass fraction of correct answers’ accuracy is 0.93. The solution proposed in the paper can be in demand in human-machine interfaces creation, medicine, marketing, and in the field of air transportation.</p>
</abstract>

This paper proposes a method of automatic speaker-independent recognition of human psycho-emotional states by analyzing the speech signal based on Deep Learning technology to solve the problems of aviation profiling. For this purpose, an algorithm to classify seven human psycho-emotional states, including anger, joy, fear, surprise, disgust, sadness, and neutral state was developed. The algorithm is based on the use of Mel-frequency cepstral coefficients and Mel spectrograms as informative features of speech signals audio recordings. These informative features are used to train two deep convolutional neural networks on the generated dataset. The developed classifier testing on a delayed verification dataset showed that the metric for the multiclass fraction of correct answers’ accuracy is 0.93. The solution proposed in the paper can be in demand in human-machine interfaces creation, medicine, marketing, and in the field of air transportation.

Aviation Profiling Method Based on Deep Learning Technology for Emotion Recognition by Speech Signal

M. Kozybayev North-Kazakhstan University Petropavlovsk

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper proposes a method of automatic speaker-independent recognition of human psycho-emotional states by analyzing the speech signal based on Deep...

Aviation Profiling Method Based on Deep Learning Technology for Emotion Recognition by Speech Signal

Data publikacji: 20 lis 2021

Zakres stron: 471 - 481

DOI: https://doi.org/10.2478/ttj-2021-0037

Słowa kluczowe
aviation profiling, emotion recognition, speech signal, neural network

© 2021 К.Т. Koshekov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Aviation Profiling Method Based on Deep Learning Technology for Emotion Recognition by Speech Signal

Data publikacji: 20 lis 2021

Zakres stron: 471 - 481

DOI: https://doi.org/10.2478/ttj-2021-0037

Słowa kluczoweaviation profiling, emotion recognition, speech signal, neural network

© 2021 К.Т. Koshekov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
aviation profiling, emotion recognition, speech signal, neural network