Automatic speech recognition (ASR) system has been developed over several decades. Although many researchers have found numerous methodologies for improving its performance, the speech processing in noisy environments remains as a difficult problem in this field. One of the solutions is using neck-microphones which are not affected by the environmental noises. However, neck-microphones distort the original voice signals since they only capture the vibrations of vocal tracts. In this context, we consider a method of enhancing features of neck-microphone signals using zero-crossings. Furthermore, we also consider using the concept of gray level co-occurrence matrix (GLCM) which has been usually used for image processing like texture analysis. In this paper, the GLCM is applied to find the suitable zero-crossing features for speech recognition. Through the simulation for speech recognition using the neck-microphone voice command system, we have shown the suggested method provides the better performance than other approaches using conventional speech features.