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We have developed an efficient feature extraction technique for speaker recognition using Radon transform (RT) and PCA. The spectrogram is compact, efficient in representation and carries information about acoustic features in the form of pattern. In the proposed method, speaker specific features have been extracted by applying image processing techniques to the pattern available in the spectrogram. Radon transform has been used to derive the effective acoustic features from the speech spectrogram. Radon transform adds up the pixel values in the given image along a straight line in a particular direction and at a specific displacement. The proposed technique computes Radon projections for seven orientations and captures the acoustic characteristics of the spectrogram. PCA applied on Radon projections yields low dimensional feature vector. The technique is computationally efficient, text-independent, robust to session variations and insensitive to additive noise.

Index Terms: Matlab, source, code, speaker, recognition, text, independent, verification, radon, transform.

 

 

 

 

Figure 1. Radon transform



A simple and effective source code for Radon Transform Speaker Recognition System.

Demo code (protected P-files) available for performance evaluation. Matlab and Matlab Signal Processing Toolbox are required.

Release
Date
Major features
1.0

2013.01.27



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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 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.

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