Using Functional Data Analysis for investigating multidimensional dynamic phonetic contrasts

In this paper we introduce a methodology based on Functional Data Analysis (FDA) that allows the investigator to delegate most of the decisions involved in the quantitative description of multidimensional time series to the data themselves. FDA produces a data-driven parametrisation of the main shape traits present in the data that is visually interpretable, in the same way as slopes or peak heights are. These output parameters are numbers that are amenable to ordinary statistical analysis, e.g. linear (mixed effects) models. FDA is also able to capture correlations among different dimensions of a time series, e.g. between formants F1 and F2. We present FDA by means of an extended case study on diphthong - hiatus distinction in Spanish, a contrast that involves duration, formant trajectories and pitch contours.