Acoustic features for estimation of perceptional similarity

Abstract

This paper describes an examination of acoustic features for the estimation of perceptional similarity between speeches. We firstly extract some acoustic features including personality from speeches of 36 persons. Secondly, we calculate each distance between extracted features using Gaussian Mixture Model (GMM) or Dynamic Time Warping (DTW), and then we sort speeches based on the physical similarity. On the other hand, there is the permutation based on the perceptional similarity which is sorted according to the subject. We evaluate the physical features by the Spearman's rank correlation coefficient with two permutations. Consequently, the results show that DTW distance with high STRAIGHT Cepstrum is an optimum feature for estimation of perceptional similarity. © Springer-Verlag Berlin Heidelberg 2007.