Tests of basic voice stress detection techniques

• Autorzy: Staroniewicz Piotr
• Języki publikacji: EN

Abstrakty

EN

The modern speech processing techniques enable new possibilities of potential applications. Besides speech and speaker recognition, also the information about speakers’ physical condition, emotional state or stress can be detected in speech signal. Since emotional stress can occur during deception, its detection in speech could be used for law or security services. The paper presents the comparative tests of two voice stress detection techniques: one based on trials of microtremors detection relying on an iterative EMD method (Empirical Mode Decomposition) and the second one based on the statistical analysis of fundamental frequency and MFCC parameters. The preliminary tests were carried on the group of 12 speakers (6 males and 6 females) answering yes/no to the list of a few dozen personal questions. The presented research revealed the speakers’ very high personal influence on the obtained results.

Słowa kluczowe

EN

Voice Stress Analysis; empirical mode decomposition

PL

analiza napięcia głosowego; VSA; empiryczna dekompozycja sygnału; EMD

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2019
• Tom: Vol. 30, nr 1
• Strony: art. no. 2019133
• Opis fizyczny: Bibliogr. 8 poz., 1 rys., 1 wykr.

Twórcy

autor

Staroniewicz Piotr

• Chair of Acoustics and Multimedia, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

Bibliografia

• 1. P. Staroniewicz, Considering basic emotional state information in speaker verification, Proc. 4th International Conference on Biometrics and Forensics (IWBF), Limmasol, Cyprus, 3-4 March 2016, IEEE 2016.
• 2. P. Staroniewicz, Automatic recognition of emotional state in Polish speech, Toward autonomous, adaptive, and context-aware multimodal interfaces: theoretical and practical issues, Lecture Notes in Computer Science, Springer, 6800 (2011) 347 - 353.
• 3. P. Staroniewicz, Recognition of emotional state in Polish speech - comparison between human and automatic efficiency, Lecture Notes in Computer Science, Springer, 5707 (2009) 33 - 40.
• 4. O. Lippold, Physiological microtremors, Scientific American, 224(3) (1971) 65 - 73.
• 5. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, H. H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Roy. Soc. Land. A, (1998) 903 - 1005.
• 6. J. Z. Zhang, N. Mbitiru, P. C. Tay, R. D. Adams, Analysis of stress in speech using Adaptive Empirical Mode Decomposition, 2009, Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers, IEEE 2009.
• 7. N. Mbitiru, P. Tay, J. Z. Zhang, R. D. Adams, Analysis of Stress in Speech Using Empirical Mode Decomposition, Proceedings of The 2008 IAJC-IJME International Conference.
• 8. C. S. Hopkins, D. S. Benincasa, R. J. Ratley, J. J. Grieco, Evaluation of voice stress analysis technology, Proceedings of the 38th Hawaii International Conference on System Sciences, IEEE 2005.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).