A wavelet filter optimization algorithm for speech recognition

A wavelet filter optimization algorithm is proposed. The proposed algorithm is based on wavelet matrix parameterization, through which wavelet filters can be factorized to a smaller set of parameters. Based on the cross validation cost function defined in this paper, Monte Carlo simulation is used to search the optimal wavelet filters for speech recognition. Using this wavelet filter optimization algorithm, we can obtain optimal wavelet filters for an isolated word recognition system. Also in this paper, mel wavelet cepstral coefficients, the speech feature generated by the optimized symmetrical shift-invariant wavelet, is described. Simulation results show that the continuous density HMM based isolated word recognition system can be optimized to achieve a recognition rate of 100% for 10 digits.