Abstract
Recent developments in computer technology have allowed the construction and widespread application of large-scale speech corpora. To enable users of speech corpora to easier data retrieval, we attempt to characterise the speaking style of speakers recorded in the corpora. We first introduce the three scales for measuring speaking style which were proposed by Eskenazi in 1993. We then use morphological features extracted from speech transcriptions that have proven effective in discriminating between styles and identifying authors 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 these stimuli and rating results, we construct an estimation model of speaking style, using a multi-regression analysis. After cross-validation (leave-1-out), the results show that among the three scales of speaking style, the ratings of two scales can be estimated with high accuracy, which proves the effectiveness of our method in the estimation of speaking style.
