Acoustic Feature-Based Clustering Analysis for Phonetic Degree of Chinese Phonogram

Abstract

A significant portion of the Chinese characters is phonogram, whose phonetic part can be used for overall sound inference. Phonetic degree is an inherent problem in the inference because low phonetic degree implies little phonetic dependence between the phonogram and its phonetic components. Solving the phonetic degree problem requires association each phonogram with the acoustic features. This paper introduces acoustic feature-based clustering, a classifying model that divides the common phonogram by defining new similarity of the sounds. This allows phonetic degree to be evaluated more reasonable. We demonstrate the clustering outperformed the traditional empirical estimation by having more accurate and real expressiveness. Acoustic feature-based clustering output 48.6% as phonetic degree, less than the empirical claim which is around 75%. As a clustering classifier, our model is competitive with a much clearer boundary on the phonogram dataset