  We use tensor analysis techniques for high-dimensional data to gain insight
into pitch curves, which play an important role in linguistics research. In
particular, we propose that demeaned phonetics pitch curve data can be modeled
as having a Kronecker product inverse covariance structure with sparse factors
corresponding to words and time. Using data from a study of native Afrikaans
speakers, we show that by targeting conditional independence through a
graphical model, we reveal relationships associated with natural properties of
words as studied by linguists. We find that words with long vowels cluster
based on whether the vowel is pronounced at the front or back of the mouth, and
words with short vowels have strong edges associated with the initial
consonant.
