Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

Selection of features is one of the important tasks in the application like Speaker Identification (SI) and other pattern recognition problems. When multiple features are extracted from the same frame of speech, it is expected that a feature vector would contain redundant features. Redundant features confuse the speaker model in multidimensional space resulting in degraded performance by the system. Careful selection of potential features can remove this redundancy while helping to achieve the higher rate of accuracy at lower computational cost. Although the selection of features is difficult without having exhaustive search, this paper proposes an alternative and straight forward technique for feature selection using Singular Value Decomposition (SVD) followed by QR Decomposition with Column Pivoting (QRcp). The idea is to capture the most salient part of the information from the speakers’ data by choosing those features that can explain different dimensions showing minimal similarities (or maximum acoustic variability) among them in orthogonal sense. The performances after selection of features using proposed criterion have been compared with using Mel-frequency Cepstral Coefficients (MFCC), Linear Frequency (LF) Cepstral Coefficients (LFCC) and a new feature proposed in this paper that is based on Gaussian shaped filters on mel-scale. It is shown that proposed SVD-QRcp based feature selection outperforms F-Ratio based method and the proposed feature extraction tool is superior to baseline MFCC & LFCC.