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In this paper, an attempt is made to propose a new feature extraction method that is capable of capturing nonlinearities in signals. For this purpose, Kernel Least Mean Square KLMS (KLMS) method is used to extract features from signal and in order to evaluate it, Hidden Markov Model (HMM) is used to model extracted feature sequence and to recognize it from other models. In HMM, Gaussian Mixture Model is used. By introducing noise on signal, results showed that recognition rate in the same level of noise is good but in other SNR values it can degrade. It is also compared with Linear Predictive Coding (LPC). Results showed that in low noise level, the proposed feature extraction has better results but in high noise level LPC has better results.

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