. . . . "Springer-Verlag" . . . . "2014-06-23+02:00"^^ . "27240" . "Classification Methods Accuracy for Speech Emotion Recognition System"@en . . "S" . "Classification Methods Accuracy for Speech Emotion Recognition System" . . "Partila, Pavol" . . "2194-5357" . . "System; Recognition; Emotion; Speech; Accuracy; Methods; Classification"@en . "RIV/61989100:27240/14:86090869" . "Classification Methods Accuracy for Speech Emotion Recognition System"@en . "[CCC876FFDE07]" . "Partila, Pavol" . "978-3-319-07400-9" . "4"^^ . "9"^^ . . . "\u0160afa\u0159\u00EDk, Jakub" . "Ostrava" . "4"^^ . . . . "Emotional state classification of human speech and recognition accuracy of the classifiers is disclosed in this paper. Recent developments in speech recognition places more emphasis on the extraction of information about the speech source. This means obtain information about who and how it was said. This article describes research which seeks to recognize the information from speaking, emotional state in particular. Emotional state is recognized by using different classifiers and features of speech by nowadays known systems. Berlin database of emotional recordings was used to train and test the system. Mel-frequency spectral coefficients and dynamic coefficients were extracted from the audio signal of the database. For classification were used Gaussian Mixture Model, k-Nearest Neighbours and Artificial Neural Networks methods. The main effort of this research is to examine the accuracy and usability of classifying methods for detection of human stress status from his speech." . "Heidelberg" . . "Tov\u00E1rek, Jarom\u00EDr" . "Voz\u0148\u00E1k, Miroslav" . "10.1007/978-3-319-07401-6_44" . "Emotional state classification of human speech and recognition accuracy of the classifiers is disclosed in this paper. Recent developments in speech recognition places more emphasis on the extraction of information about the speech source. This means obtain information about who and how it was said. This article describes research which seeks to recognize the information from speaking, emotional state in particular. Emotional state is recognized by using different classifiers and features of speech by nowadays known systems. Berlin database of emotional recordings was used to train and test the system. Mel-frequency spectral coefficients and dynamic coefficients were extracted from the audio signal of the database. For classification were used Gaussian Mixture Model, k-Nearest Neighbours and Artificial Neural Networks methods. The main effort of this research is to examine the accuracy and usability of classifying methods for detection of human stress status from his speech."@en . "RIV/61989100:27240/14:86090869!RIV15-MSM-27240___" . . "Nostradamus 2014: prediction, modeling and analysis of complex systems" . . "7445" . . . "Classification Methods Accuracy for Speech Emotion Recognition System" .