A fast pitch search algorithm using the skipping technique is proposed to reduce the computation time in CELP vocoder. Based on the characteristics of the correlation function of speech signal, the proposed algorithm skips over certain ranges in the full pitch search range in a simple way. Though the search range is reduced, high speech quality can be maintained since those lags having high correlation values are not skipped over and are used for search by closed-loop analysis. To improve the efficiency of the proposed method, we develop three variants of the skipping technique. The experimental results show that the proposed and the modified algorithm can reduce the computation time in the pitch search considerably, over 60% reduction compared with the traditional full search method.

This paper considers the estimation of speech parameters and their enhancement using an approach based on the estimation-maximization (EM) algorithm, when only noisy speech data is available. The distribution of the excitation source for the speech signal is assumed as a mixture of two Gaussian probability distribution functions with differing variances. This mixture assumption is experimentally valid for removing the residual excitation signal. The assumption also is found to be effective in enhancing noise-corrupted speech. We adaptively estimate the speech parameters and analyze the characteristics of its excitation source in a sequential manner. In the maximum likelihood estimation scheme we utilize the EM algorithm, and employ a detection and an estimation step for the parameters. For speech enhancement we use Kalman filtering for the parameters obtained from the above estimation procedure. The estimation and maximization procedures are closely coupled. Simulation results using synthetic and real speech vindicate the improved performance of our algorithm in noisy situations, with an increase of about 3 dB in terms of output SNR compared to conventional Gaussian assumption. The proposed algorithm also may be noteworthy in that it needs no voiced/unvoiced decision logic, due to the use of the residual approach.

A new steepest descent linear adaptive algorithm, called the proportion-sign algorithm (PSA), is introduced and its performance analysis is presented when the signals are from zero-mean jointly stationary Gaussian processes. The PSA improves the convergence speed over the least mean square (LMS) algorithm without overly degrading the steady-state error performance and has the robustness to impulsive interference occurring in the desired response by adding a minimal amount of computational complexity. Computer simulations are presented that show these advantages of the PSA over the LMS algorithm and demonstrate a close match between theoretical and empirical results to verify our analysis.