Feature Reduction Method for Speaker Identification Systems Using Particle Swarm Optimization

Feature selection (FS) is a process in which the most informative and descriptive characteristics of a signal that will lead to better classification are chosen. The process is utilized in many areas, such as machine learning, pattern recognition and signal processing. FS reduces the dimensionality of a signal and preserves the most informative features for further processing. A speech signal can consist of thousands of features. Feature extraction methods such as Average Framing Linear Prediction Coding (AFLPC) using wavelet transform reduce the number of features from thousands to hundreds. However, the vector of features involves some redundancy. In addition, some features are similar and do not give discrimination to classes. Taking such features into consideration in the classification process will not help to identify certain classes; conversely, they will only serve to confuse the classifier and inhibit identification of accurate classes. This paper proposes an FS method that uses evolution optimization techniques to select the most informative features that maximize the classification rates of Bayesian classifiers. The classification rate is also maximized by modeling the features with the proper number of Gaussian distributions. The results of comparative analysis conducted show that the selection based individual speaker model gives the best classification rate performance. Keyword Feature Selection, Speaker Identification, Bayes Theorem.