Features extracted from the surface electromyography (sEMG) signals during the speaking tasks play an essential role in sEMG based speech recognition. However, currently there are no general rules on the optimal choice of sEMG features to achieve satisfactory performance. In this study, a total of 120 electrodes were placed on the face and neck muscles to record the high-density (HD) sEMG signals when subjects spoke ten digits in English. Then ten different time-domain features were computed from the HD sEMG signals and the classification performance of the speech recognition was thoroughly compared. The contribution of each feature was examined by using three performance metrics, which include classification accuracy, sensitivity, and F1-Score. The results showed that, among all the ten different features, the features of WFL, MAV, RMS, and LOGD were considered to be superior because they achieved higher classification accuracies with high sensitivities and higher F1-Scores across subjects/trials in the sEMG-based digit recognition tasks. The findings of this study might be of great value to choose proper signal features that are fed into the classifier in sEMG-based speech recognition.