Search for: All records

Cluster analysis on time-series f0 data is an increasingly popular method in intonation research. There are a number of methodological decisions to take when applying cluster analysis. Crucially, these decisions may affect the clustering results, potentially also the conclusions of the research. This paper investigates the extent to which the choice for either K-means or hierarchical clustering, two of the most popular clustering methods, leads to grouping differences that are potentially relevant for intonation research. This is tested using a dataset of f0 measures taken from imitated intonation patterns in American English. The analysis concerns a generic correlation test between K-means and hierarchical clustering outcomes as well as a number of specific measures assessing partitioning quality and f0 contour differences. The results show that both cluster methods generally show very similar outcomes, although considerable differences for specific clusterings might occur.