Comparing genetic algorithms to principal component analysis and linear discriminant analysis in reducing feature dimensionality for speaker recognition

Mel-Frequency Cepstral Coefficients and their derivatives are commonly used as acoustic features for speaker recognition. Reducing the dimensionality of the feature set leads to more robust estimates of the model parameters, and speeds up the classification task, which is crucial for real-time speaker recognition applications running on low-resource devices. In this paper, a feature selection procedure based on genetic algorithms (GA) is presented and compared to two well-known dimensionality reduction techniques, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Evaluation is carried out for two speech databases, containing laboratory read speech and telephone spontaneous speech, and applying a state-of-the-art speaker recognition system. GA-based feature selection outperformed PCA and LDA when dealing with clean speech, but not for telephone speech, probably due to some noise compensation implicit in linear transforms, which cannot be accomplished just by selecting a subset of features.