Sparse linear prediction coefficients for isolated speech recognition

Isolated speech recognition system is an important step in many applications such as automated banking system, catalogue dialing, automated data entry, robotics etc. Selection of feature and classifier in the speech recognition system is based on the complexity and recognition accuracy. Mel-frequency cepstral coefficients (MFCCs), line spectral frequencies (LSF), short time energy (STE) and linear prediction coefficients (LPC) are the features used in the existing speech recognition systems. In this paper, a sparse feature, obtained from the optimization of linear prediction coefficients (LPC) with a sparsity constraint is used for the classification. These sparse linear prediction coefficients (sparse LPC) offer a more effective way of representing the voiced speech. Artificial neural network (ANN) is used for the classification purpose. Experimental results show that the proposed method is noise robust and its performance exceeds LPC and MFCC feature based speech recognition systems.