Human emotion plays a major role in expressing their feelings through speech. Emotional speech recognition is an important research ¯eld in the human–computer interaction. Ultimately, the endowing machines that perceive the users' emotions will enable a more intuitive and reliable interaction.The researchers presented many models to recognize the human emotion from the speech. One of the famous models is the Gaussian mixture model (GMM). Nevertheless, GMM may sometimes have one or more of its components as ill-conditioned or singular covariance matrices when the number of features is high and some features are correlated. In this research, a new system based on a weighted distance optimization (WDO) has been developed for recognizing the emotional speech. The main purpose of the WDO system (WDOS) is to address the GMM shortcomings and increase the recognition accuracy. We found that WDOS has achieved considerable success through a comparative study of all emotional states and the individual emotional state characteristics. WDOS has a superior performance accuracy of 86.03% for the Japanese language. It improves the Japanese emotion recognition accuracy by 18.43% compared with GMM and k-mean.