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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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Release |
Date |
Major features |
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1.1 |
2006.07.12 |
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1.0 |
2006.06.14 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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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.
