Nowadays automated segmentation of speech signals has been attracted many of the researchers all-over the world. Many speech processing systems require segmentation of speech waveform into principal acoustic units. In this research, TIMIT DataBase (DB) is utilized to carry on this process and justify its operation or results. Thus, this paper presents a novel method of segmentation of speech phonemes, where the proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation. There are three main techniques of feature extraction used in our research; the first technique is the Mel Frequency Cepstral Coefficient (MFCC), the second technique is known by Best Tree Encoding (BTE), while the third is Image Normalized Encoder (INE), which is a hybrid technique between the Best Tree Image (BTI), and the Convolution Neural Network (CNN) ResNet-50. Then, data are trained using a hybrid model that consists of Hidden Markov Model (HMM), and Gaussian Mixture Model (GMM) to improve the performance of automatic speech recognition. The proposed model is tested and verified against the most widely used feature MFCC plus delta and delta-delta coefficients (39 parameters) to evaluate its performance. This approach has the potential to be used in applications such as automatic speech recognition and automatic language identification. The experimental results show that BTE technique achieved the highest success rate ( ) (92.64%) than using the INE technique. However, the INE technique gives confusion success rate for Transition (Tr) and Non-Transition (NTr) of values 97.1% and 99.1%, respectively.