Recent developments in computer technology have allowed the construction and widespread application of large-scale speech corpora. To foster ease of data retrieval for people interested in utilising these speech corpora, we attempt to characterise speaking style across some of them. In this paper, we first introduce the 3 scales of speaking style proposed by Eskenazi in 1993. We then use morphological features extracted from speech transcriptions that have proven effective in style discrimination and author identification in the field of natural language processing to construct an estimation model of speaking style. More specifically, we randomly choose transcriptions from various speech corpora as text stimuli with which to conduct a rating experiment on speaking style perception; then, using the features extracted from those stimuli and the rating results, we construct an estimation model of speaking style by a multi-regression analysis. After the cross validation (leave-1-out), the results show that among the 3 scales of speaking style, the ratings of 2 scales can be estimated with high accuracies, which prove the effectiveness of our method in the estimation of speaking style.