Emotional Impact on Neurological Characteristics and Human Speech
This article discusses impact of human emotions on physiological characteristics and their changes. Many fields require applications that provide information about the emotional state of a human. Today’s research is mainly concerned with increasing the accuracy of the methodology for obtaining this information. Studied subjects were psychologically stimulated to change their neutral calm state to stress. Subjects were measured physiological characteristics and the change of speech also. Blood samples, ECG and EEG form part of the neurophysiological data that were collected during the neutral state and during stress. Voice activity was recorded from reading text that read, patients in both emotional state. Features extraction was focused on the Mel-frequency Cepstral coefficients and their dynamic and accelerated derivations. Change in emotional state from neutral to stress was recognized by using a GMM classifier that has been trained and tested by mentioned speech features. Psychological stimulus was induced using professional psychological methods. The measurement was performed in a special EMC interference protected chamber to prevent undesirable electrical influences from the external environment especially on sensitive EEG measurement.
KeywordsEmotional state cortisol ECG EEG MFCC GMM
Unable to display preview. Download preview PDF.
- 1.Iliou, T., Anagnostopoulos, C.-N., Narayanan, S.: Comparison of Different Classifiers for Emotion Recognition. In: 2009 13th Panhellenic Conference on Informatics, pp. 102–106. IEEE (2009), doi:10.1109/PCI.2009.7Google Scholar
- 2.Zulfiqar, A., Muhammad, A., Martinez Enriquez, A.M.: A Speaker Identification System Using MFCC Features with VQ Technique. In: 2009 Third International Symposium on Intelligent Information Technology Application, pp. 115–118. IEEE (2009), doi:10.1109/IITA.2009.420Google Scholar
- 3.Jayanna, H.S., Mahadeva Prasanna, S.R., Martinez Enriquez, A.M.: Analysis, Feature Extraction, Modeling and Testing Techniques for Speaker Recognition. IETE Technical Review 26(3), 181 (2009), doi:10.4103/0256-4602.50702Google Scholar
- 4.Hossan, A., Sheeraz, M., Gregory, M.A.: A novel approach for MFCC feature extraction. In: 2010 4th International Conference on Signal Processing and Communication Systems, vol. 26(3), p. 181. IEEE (2010), doi:10.1109/ICSPCS.2010.5709752Google Scholar
- 5.Schuller, B., Vlasenko, B., Eyben, F., Rigoll, G., Wendemuth, A.: Acoustic emotion recognition: A benchmark comparison of performances. In: 2009 IEEE Workshop on Automatic Speech Recognition, vol. 26(3), pp. 552–557. IEEE (2009), doi:10.1109/ASRU.2009.5372886Google Scholar
- 6.Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A Database of German Emotional Speech. In: Proc. Interspeech, Lisbon, pp. 1517–1520 (2005)Google Scholar
- 7.Metallinou, B., Lee, S., Narayanan, S., Rigoll, G., Wendemuth, A.: Audio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice: A benchmark comparison of performances. In: 2008 Tenth IEEE International Symposium on Multimedia, vol. 26(3), pp. 250–257. IEEE (2008), doi:10.1109/ISM.2008.40Google Scholar
- 8.Metallinou, A., Lee, S., Narayanan, S., Rigoll, G., Wendemuth, A.: Audio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice: A benchmark comparison of performances. In: 2008 Tenth IEEE International Symposium on Multimedia, vol. 26(3), pp. 250–257. IEEE (2009), doi:10.1109/ICAPR.2009.89Google Scholar