Redescribing intonational categories with functional data analysis

Intonational research is often dependent upon hand-labeling by trained listeners, which can be prone to bias or error. We apply tools from Functional Data Analysis (FDA) to a set of fundamental frequency (F0) data to demonstrate how these tools can provide a less theory-dependent way of investigating F0 contours by allowing statistical analyses of whole contours rather than depending on theoretically-determined “important” parts of the signal. The results of this analysis support the predictions of current intonational phonology while also providing additional information about phonetic variability in the F0 contours that these theories do not currently model.