Speech rhythm measurements have been used in a limited number of previous studies on automated speech assessment, an approach using speech recognition technology to judge non-native speakers' proficiency levels. However, one of the most problematic issues of these previous studies is a lack of a comparison of these rhythm features with other effective non-rhythm features found in decade-long previous research. In this paper, we extracted both non-rhythm and rhythm features and compared them with respect to their performances to predict proficiency scores rated by humans. We show that adding rhythm features significantly improves the performance of the scoring model based only on non-rhythm features.