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Phones for 239 non-annotated languages were selected by automatic segmentation based on changes of energy in the time-frequency representation of speech signals. Phone boundaries were set at location of relatively major changes in energy distribution between seven frequency bands. A vector of average energies calculated for eleven frequency bands was chosen as the representation of a single phone. We focus our research on an unsupervised comparison of phone distribution in 239 languages. Using the hierarchical clustering method, the relationship between the number of clusters and Ward’s distance was determined. A mathematical model is proposed to describe this dependency. Its four parameters are determined for each language individually to model the relationship between the number of clusters and the frequency diversity of phones contained in clusters. We used these relationships to compare languages and to create their ranking based on the size of phone varieties in the frequency domain.

Language ranking based on frequency varieties of phones