Authors Yu. A. Bryuhomitsky, V. M. Fedorov
Month, Year 08, 2018 @en
Index UDC 004.067
DOI 10.23683/2311-3103-2018-8-173-181
Abstract An immunological method is proposed for solving the problem of text-independent identification of a person by voice, based on the principles of presentation and processing of voice information accepted in artificial immune systems. For personality identification by its voice, a Fanta model is used in which the voice signal is formed by passing through a high-order filter. Cepstral coefficients obtained on the basis of a linear speech predictor are used as feature vectors. The following analysis of the feature vectors is carried out on the basis of the apparatus of artificial immune systems using an immunological model of negative selection. The model implements decentralized recognition of sequentially reaching speech fragments by comparing them with special, previously created recognition elements — detectors which imitate the immune-competent cells of the immune system. The matching is carried out using the Euclidean proximity measure according to the principle of negative selection. During the speech signal analysis, the decision "well-known/stranger" is making based on a statistics of detectors response frequency. The method has been experimentally tested in IDE MATLAB and showed its effectiveness. The method is intended for continuous authentication control of the speaker’s identity at the rate of voice data income when text of arbitrary size and content is reproduced. It allows making a timely decision about the possible substitution of speakers. The advantage of the method is its complete protection from replay attacks. Effective implementation of the method, its increasing accuracy are closely related to the possibility of organizing the parallel calculations of large amounts of data, due to the size of the analyzed text and the size of detectors population. This circumstance determines the perspective of using high-performance multiprocessor computing systems.

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Keywords Text-independent identification by voice; cepstral analysis; linear speech signal predictor; artificial immune systems; negative selection model.
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