  In spontaneous speech, Mandarin tones that belong to the same tone category
may exhibit many different contour shapes. We explore the use of data mining
and NLP techniques for understanding the variability of tones in a large corpus
of Mandarin newscast speech. First, we adapt a graph-based approach to
characterize the clusters (fuzzy types) of tone contour shapes observed in each
tone n-gram category. Second, we show correlations between these realized
contour shape types and a bag of automatically extracted linguistic features.
We discuss the implications of the current study within the context of
phonological and information theory.
