Text-Independent Speaker Recognition Based on Neural Networks
Speaker recognition or voice recognition is the task of recognizing people from their voices. Such systems extract features from speech, model them and use them to recognize the person from his/her voice. Speaker recognition has a history dating back some four decades, where the output of several analog filters was averaged over time for matching. Speaker recognition uses the acoustic features of speech that have been found to differ between individuals. These acoustic patterns reflect both anatomy (e.g., size and shape of the throat and mouth) and learned behavioral patterns (e.g., voice pitch, speaking style). This incorporation of learned patterns into the voice templates (the latter called “voiceprints”) has earned speaker recognition its classification as a “behavioral biometric.”
Speaker recognition systems employ three styles of spoken input: text-dependent, text-prompted and text-independent. Most speaker verification applications use text-dependent input, which involves selection and enrollment of one or more voice passwords. Text-prompted input is used whenever there is concern of imposters. The various technologies used to process and store voiceprints includes hidden Markov models, pattern matching algorithms, neural networks, matrix representation and decision trees. Some systems also use “anti-speaker” techniques, such as cohort models, and world models. Ambient noise levels can impede both collection of the initial and subsequent voice samples. Performance degradation can result from changes in behavioral attributes of the voice and from enrollment using one telephone and verification on another telephone. Voice changes due to aging also need to be addressed by recognition systems.
Many companies market speaker recognition engines, often as part of large voice processing, control and switching systems. Capture of the biometric is seen as non-invasive. The technology needs little additional hardware by using existing microphones and voice-transmission technology allowing recognition over long distances via ordinary telephones (wire line or wireless). Multi-layered networks are capable of performing just about any linear or nonlinear computation, and can approximate any reasonable function arbitrarily well. Such networks overcome the problems associated with the perceptron and linear networks. However, while the network being trained may be theoretically capable of performing correctly, back propagation and its variations may not always find a solution. There are many types of neural networks for various applications multilayered perceptrons (MLPs) are feedforward networks and universal approximators. They are the simplest and therefore most commonly used neural network architectures.
Index Terms: Matlab, speaker recognition, speaker verification, speaker matching, neural networks, feature extraction, ann, artificial neural networks, nn.
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Figure 1. Speech signal |
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A simple and effective source code for Speaker Identification based on Neural Networks. |
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Demo code (protected P-files) available for performance evaluation. Matlab Signal Processing Toolbox and Matlab Neural Network Toolbox are required.
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1.1
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2006.07.12
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1.0
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2006.06.14
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Speaker Recognition System Based on ANN – Release 1.0 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 150 EUROS (less than 210 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Speaker Recognition System Based on ANN. Alternatively, you can bestow using our banking coordinates:
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This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab Signal Processing Toolbox and Matlab Neural Network Toolbox are required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
Neural Network Technology
Having an easier life by the help of developing technologies forces people is more complicated technological structure. In today’s world, security is more important than ever. Dazziling developments in technology arouse interest of scientists about human and human behaviors and at the same time, give an opportunity to people to apply their thoughts. Today, for security needs, detailed researches are organized to set up the most reliable system. Iris Recognition Security System is one of the most reliable leading technologies that most people are related. Iris recognition technology combines computer vision, pattern recognition, statistical inference, and optics. Its purpose is real-time, high confidence recognition of a person’s identity by mathematical analysis of the random patterns that are visible within the iris of an eye from some distance. Because the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a kind of living passport or a living password that one need not remember but can always present. Because the randomness of iris patterns has very high dimensionality, recognition decisions are made with confidence levels high enough to support rapid and reliable exhaustive searches through national-sized databases.
Artificial Neural Networks (ANNs) are programs designed to simulate the way a simple biological nervous system is believed to operate. They are based on simulated nerve cells or neurons, which are joined together in a variety of ways to form networks. These networks have the capacity to learn, memorize and create relationships amongst data. ANN is an information-processing paradigm, implemented in hardware or software that is modeled after the biological processes of the brain. An ANN is made up of a collection of highly interconnected nodes, called neurons or processing elements. A node receives weighted inputs from other nodes, sums these inputs, and propagates this sum through a function to other nodes. This process is analogous to the actions of a biological neuron. An ANN learns by example. In a biological brain, learning is accomplished as the strengths of the connections between nodes are adjusted. This is true for ANN’s also, as these strengths are captured by the weights between the nodes. ANN’s most important advantage is that they can be used to solve problems of considerable complexity; problems that do not have an algorithmic solution or for which such a solution is too complex to be found. Because of their abstraction from the brain, ANNs are good at solving problems that humans are good at solving but which computers are not. Pattern recognition and classification are examples of problems that are well suited for ANN application.
Index Terms: Matlab, source, code, iris, recognition, segmentation, detection, verification, matching, ann, nn, neural, network, networks.
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Figure 1. Neural network example |
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A simple and effective source code for Personal Iris Recognition Using Neural Network. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required.
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1.0
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2008.12.15
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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Personal Iris Recognition Using Neural Network – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 250 EUROS (less than 350 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of Personal Iris Recognition Using Neural Network. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks
High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.
Zhengjun Pan and Hamid Bolouri, “High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks”, 1999.
Index Terms: Face recognition, neural networks, feature extraction, discrete cosine transform, face matching, face identification, dct, ann, artificial neural networks, nn.
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Figure 1. Architecture of neural networks |
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A simple and effective source code for Face Identification based on DCT and Neural Networks. All tests were performed with AT&T face database available here. A complete list of public face databases is available at http://www.advancedsourcecode.com/facedatabase.asp. |
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Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required.
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1.0
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2006.05.16 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. |
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DCT-ANN Based Face Recognition System – Release 1.0 – Click here for your donation. In order to obtain the source code you have to pay a little sum of money: 30 EUROS (less than 42 U.S. Dollars).
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Once you have done this, please email us luigi.rosa@tiscali.it
As soon as possible (in a few days) you will receive our new release of DCT-ANN Based Face Recognition System. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 14 SP1. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required. The code provided has to be considered “as is” and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.
Popularity: 1% [?]
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