TITLE:
Stand-Alone Intelligent Voice Recognition System
AUTHORS:
Mohammed R. Saady, Hatem El-Borey, El-Sayed A. El-Dahshan, Ashraf Shamseldin Yahia
KEYWORDS:
Voice Recognition, Wavelet Packet Transform, Feature Extraction, Artificial Neural Network
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.5 No.4,
November
18,
2014
ABSTRACT: 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 systemconsists
of a combination of three stages: signal pre-processing, features extraction by
usingWavelet 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.