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We use tensor analysis techniques for high-dimensional data to gain insightinto pitch curves, which play an important role in linguistics research. Inparticular, we propose that demeaned phonetics pitch curve data can be modeledas having a Kronecker product inverse covariance structure with sparse factorscorresponding to words and time. Using data from a study of native Afrikaansspeakers, we show that by targeting conditional independence through agraphical model, we reveal relationships associated with natural properties ofwords as studied by linguists. We find that words with long vowels clusterbased on whether the vowel is pronounced at the front or back of the mouth, andwords with short vowels have strong edges associated with the initialconsonant.

Tensor models for linguistics pitch curve data of native speakers of Afrikaans