In this study a perception experiment was carried out to examine the
perceived similarity of intonation contours. Amongst other results we
found, that the subjects are capable to produce consistent similarity
judgements.

On the basis of this data we studied the influence of several physical
distance measures on the human similarity judgements by grouping these
measures to principal components and by comparing the weights of these
components in a linear regression model predicting human
perception. Non-correlation based distance measures for f0 contours
received the highest relative weight.

Finally, we developed applicable linear regression and neural feed
forward network models predicting similarity perception of intonation
on the basis of physical contour distances. The performance of the
neural networks, measured in terms of mean absolute error, did not
differ significantly from the human performance derived from judgement
consistency.