Abstract
The goal of the speech segments extraction process is to separate acoustic events of interest (the speech segment to be recognised) in a continuously recorded signal from other parts of the signal (background). The recognition rate of many voice command systems is very much dependent on speech segment extraction accuracy. This paper discusses two novel HMM based techniques that segregate a speech segment from its concurrent background. The first method can be reliably used in clean environments while the second method, which makes use of the wavelets denoising technique, is effective in noisy environments. These methods have been implemented and shown superiority over other popular techniques, thus, indicating that they have the potential to achieve greater levels of accuracy in speech recognition rates.