Article 8215

Title of the article



Alimuradov Alan Kazanferovich, Postgraduate student, Penza State University (40 Krasnaya street, Penza, Russia),
Churakov Petr Pavlovich, Doctor of engineering sciences, professor, sub-department of information-measuring technology and metrology, Penza State University (40 Krasnaya street, Penza, Russia),

Index UDK



Background. The research object is the stages of speech signals processing, ap-plied in speech recognition systems. The research subject is the problem of "sig-nal/pause" segmentation that represents detection of signal and pauses’ boundaries in the overall flow of speech signals. The purpose of the work is to develop and re-search a noise-eliminating algorithm of "signal/pause" segmentation, adaptive to ag-gressively noisy environment.
Materials and methods. At development of the algorithm the following methods were used: a method of adaptive processing of nonlinear and non-stationary signals – the Complementary Multiple Empirical Mode Decomposition; a statistical data proc-essing method – the Independent Component Analysis; a differentiation method us-ing the concepts of normal distribution and one-dimensional Mahalanobis distance.
Results. The noise-eliminating algorithm of "signal/pause" segmentation for speech recognition systems was developed and investigated. The article adduces a flowchart of the algorithm with a detailed mathematical description. The work speci-fies the advantages compared with the known algorithms of "signal/pause" segmentation, used in speech recognition systems. The developed algorithm increases the valid detection coefficient by 13 % on average.
Conclusions. Comparison of the research results allows to conclude that the developed noise-eliminating algorithm of "signal/pause" segmentation is recommended for practical application in speech recognition systems, used in aggressively noisy environment.

Key words

processing of speech signals, "signal/pause" segmentation, speech recognition, complementary multiple empirical mode decomposition.

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