Modern speech processing applications require operation on signal of interest that is contaminated by high level of noise. This situation calls for a greater robustness in estimation of the speech parameters, a task which is hard to achieve using standard speech models. In this paper, we present an optimal estimation procedure for sound signals (such as speech) that are modeled by harmonic sources. The harmonic model achieves more robust and accurate estimation of voiced speech parameters. Using maximum a posteriori probability framework, successful tracking of pitch parameters is possible in ultra low signal to noise conditions (as low as -15 dB). The performance of the method is evaluated using the Keele pitch detection database with realistic background noise. The results show best performance in comparison to other state-of-the-art pitch detectors. Application of the proposed algorithm in a simple speaker identification system shows significant improvement in the performance.

In this paper we present a procedure for estimating the parameters of speech signals that are contaminated by high level of noise. The proposed estimation method is developed by assuming a harmonic model for the voiced frame hypothesis. A Maximum A-posteriori Probability tracking method is developed for estimating time-varying pitch. Signal recoIl8truction is achieved by projecting the signal onto the subspace of harmonic signals with the optimal estimates of the fundamental frequency. The performance of the proposed method is evaluated and compared to other existing methods using a large pitch detection database. It is shown that the proposed method for pitch estimation is more robust and much more accurate in terms of mean-square-error and gross error rate, in comparison to other existing methods, specially at ultra low signal-to-noise ratios (as low as −15 dB). Examples of speech reconstruction/enhancement are also presented in the paper.