Speaker recognition based on pre-processing approaches

Abstract

This paper presents two pre-processing methods that can be implemented for noise reduction in speaker recognition systems. These methods are adaptive noise canceller (ANC) and Savitzky-Golay (SG) filter. Also, discrete cosine transform (DCT), discrete wavelet transform (DWT) and discrete sine transform (DST) are considered for consistent feature extraction from noisy speech signals. A neural network with only one hidden layer is used as a classifier. The performances of the proposed noise reduction methods are compared with those of a hybrid method that comprises empirical mode decomposition (EMD) and spectral subtraction and also with spectral subtraction method only. Recognition rate is taken as a performance metric to evaluate the behavior of the system with these enhancement strategies. Simulation results prove that the DCT is the optimum transform with the suggested methods, while the DWT is the best one with the hybrid method and the spectral subtraction method.