Master ' s Thesis in Computational Linguistics Jan

Cluster analysis is the name for a group of multivariate techniques whose primary purpose is to group objects based on the characteristics they possess. Clustering has been applied in many contexts and by researchers in many disciplines. This reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. In this thesis I explore cluster analysis as a means to investigate relationships among speech samples extracted from the Swedia 2000 dialect database. By using mel frequency cepstral coefficients (MFCC) to represent the data, I also show that the cepstrum is a reliable metric for measuring acoustic distance. The methods applied in this thesis may contribute to the validation of dialect distances, and finding the optimal number of clusters in a dialect data set. The results show that this approach may represent an effective tool to support speech analysis applications.