A system of algorithms for stable human recognition

Desyatchikov, A. A.; Kovkov, D. V.; Lobantsov, V. V.; Makovkin, K. A.; Матвеев, И. А.; Мурынин, А. Б.; Chuchupal, V. Ya.

doi:10.1134/s1064230706060116

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…The security system based on iris or fingerprint is not convenient in practice since it imposes stringent requirements on interaction with the user; the users of modern fingerprint become anxious about the hygiene of the process, and the users of modern iris face stringent requirements on movements and visibility of the eyes [1]. So there is a strong motivation to work in security system based on voice.…”

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confidence: 99%

Stand-Alone Intelligent Voice Recognition System

Saady¹,

El-Borey²,

El‐Dahshan³

et al. 2014

JSIP

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In this paper, an expert system for security based on biometric human features that can be obtained without any contact with the registering sensor is presented. These features are extracted from human's voice, so the system is called Voice Recognition System (VRS). The proposed system consists of a combination of three stages: signal pre-processing, features extraction by using Wavelet Packet Transform (WPT) and features matching by using Artificial Neural Networks (ANNs). The features vectors are formed after two steps: firstly, decomposing the speech signal at level 7 with Daubechies 20-tap (db20), secondly, the energy corresponding to each WPT node is calculated which collected to form a features vector. One hundred twenty eight features vector for each speaker was fed to the Feed Forward Back-propagation Neural Network (FFBPNN). The data used in this paper are drawn from the English Language Speech Database for Speaker Recognition (ELSDSR) database which composes of audio files for training and other files for testing. The performance of the proposed system is evaluated by using the test files. Our results showed that the rate of correct recognition of the proposed system is about 100% for training files and 95.7% for one testing file for each speaker from the ELSDSR database. The proposed method showed efficiency results were better than the well-known Mel Frequency Cepstral Coefficient (MFCC) and the Zak transform.

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confidence: 99%