Feature extraction based on zero-crossings with peak amplitudes for robust speech recognition in noisy environments

The ensemble interval histogram (EIH) is an auditory model which can be used as a robust “front-end” for speech recognition systems. The utilization of multiple level-crossing detectors in the EIH provides frequency and intensity information, which may be useful for speech processing. Proper determination of the number of levels and the level values is very important for reliable performance of the system. An analytic relationship is developed for the variance and SNR of the level-crossing intervals as a function of the crossing level value, and a new feature extraction method based on zero-crossings with peak amplitudes is proposed for robust speech recognition in noisy environments. The proposed method not only can preserve intensity information, but also is robust to noise in estimating the frequency information without the need to determine the level values and the number of levels. Experimental results show the robustness of the proposed method