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The efforts to automate the combination of expert opinions have been studied extensively in the second half of the twentieth century.
The combined experts are classifiers and the result of the
combination is also a classifier. The outputs of classifiers can be represented
as vectors of numbers where the dimension of vectors is equal to the number
of classes. As a result, the combination problem can be defined as a problem of
finding the combination function accepting N-dimensional score vectors from
M classifiers and outputting N final classification scores, where the
function is optimal in some sense, e.g. minimizing the misclassification cost.
Combination methods can also be grouped based on the level at which they operate.
Combinations of the first type operate at the feature level. The features
of each classifier are combined to form a joint feature vector and classification is subsequently performed in the new feature space.
Combinations can also operate at the decision or score level, that is they
use outputs of the classifiers for combination. This is a popular approach because the knowledge
of the internal structure of classifiers and their feature
vectors is not needed. Though there is a possibility that representational information
is lost during such combinations, this is usually compensated by
the lower complexity of the combination method and superior training of the
final system.
We have developed a fast and reliable algorithm for speech recognition for isolated words.
The proposed method combines at decision level several algorithms commonly used for speech recognition such as Discrete
Cosine Transform, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding, Relative Spectral Transform and Perceptual Linear Prediction.
The algorithm for combination can be easily parallelized and run on low-cost hardware in reasonable time.
Index Terms: Matlab, source, code, speech, recognition, isolated, word, words, feature, algorithm, combination, fusion.
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Figure 1. Speech waveform |
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A simple and effective source code for Hybrid Speech Recognition System. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Wavelet Toolbox and Matlab Signal Processing Toolbox are required. |
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Release |
Date |
Major features |
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1.0 |
2013.12.16 |
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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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Hybrid Speech Recognition System - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 400 EUROS (less
than 560 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 Hybrid Speech 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 2006a. Matlab Image Processing Toolbox, Matlab Wavelet 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.
