Abstract
The authors analyze the models based on deep learning neural networks on the basis of the general approach to pauses and speech signals as different types of audio information fixed in a phonogram, different in some characteristics. It is shown that such an approach allows generating the learning database with the use of the general for pauses and signals of speech methods of preliminary processing of information. This provides a higher level of unification of network learning methods intended for solution of various examination problems.
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References
V. I. Solovyov, O. V. Rybalskiy, and V. V. Zhuravel, “Verification of fundamental fitness of neuron networks of the deep educating for the construction of the system of exposure of editing of digital phonograms,” Cybern. Syst. Analysis. Vol. 56, No. 2, 326–330 (2020). https://doi.org/10.1007/s10559-020-00249-2.
O. Rybalskyi, V. Soloviov, S. Cherniavskyi, and V. Zhuravel, “Features of modern probabilistic technologies of judicial examination,” Nauka i Pravookhorona, Iss. 4 (46), 343–349 (2019). https://doi.org/10.36486/np.2019.4(46).39.
O. Rybalsky, V. Solovyov, and V. Zhuravel, “The systems of tool of examination of audio and videotape recording are in Ukraine,” Vestn. Polotsk. Gos. Un-ta, Ser. C, Fundamental’nyye Nauki, No. 4, 15–19 (2018).
O. V. Rybalsky and Yu. F. Zharikov, Modern Methods for Verifying the Authenticity of Magnetic Phonograms in Forensic Acoustic Examination [in Russian], NAVSU, Kyiv (2003).
V. I. Solovyov, O. V. Rybalskiy, and V. V. Zhuravel, “Method of exposure of signs of the digital editing in phonograms with the use of neuron networks of the deep learning,” J. Autom. Inform. Sci., Vol. 52, Iss. 1, 22–28. (2020). https://doi.org/10.1615/JAutomatInfScien.v52.i1.30.
O. V. Rybalskyi, V. I. Solovev, and V. V. Zhuravel, “Automatic segmentation of phonograms by pauses in speech flow,” Modern Special Technics, No. 1, 58–64 (2018).
N. V. Semenova, L. N. Kolechkina, and A. M. Nagirna, “Vector optimization problems with linear criteria over a fuzzy combinatorial set of alternatives,” Cybern. Syst. Analysis, Vol. 47, No. 2, 250–259 (2011). https://doi.org/10.1007/s10559-011-9307-5.
S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego (2005).
M. A. Sapozhkov, Electroacoustics [in Russian], Svyaz’, Moscow (1978).
Yu. I. Alexandrov, Psychophysiology [in Russian], Nauka, Moscow–St. Petersburg (2006).
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Translated from Kibernetyka ta Systemnyi Analiz, No. 1, January–February, 2021, pp. 153–159.
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Solovyov, V.I., Rybalskiy, O.V., Zhuravel, V.V. et al. Analyzing the Models of Speech Recognition on the Basis of Neural Networks of Deep Learning for Examination of Digital Phonograms. Cybern Syst Anal 57, 133–138 (2021). https://doi.org/10.1007/s10559-021-00336-y
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DOI: https://doi.org/10.1007/s10559-021-00336-y