Detecting changes in speech expressiveness in participants of a radio program

Abstract

A method for speech expressiveness change detection is presented which combines a dimensional analysis of speech expression, a Principal Component Analysis technique, as well as multiple regression analysis. From the three inferred rates of activation, valence, and involvement, two PCA-factors explain 97 % of the variance of the judges’ evaluations of a corpus of radio show interaction. The multiple regression analysis predicted the values of the two listener-oriented, PCA-derived dimensions of promptness and empathy from the acoustic parameters automatically obtained from a set of 206 utterances produced by radio show’s participants. Analysed chronologically, the utterances reveal expression change from automatic acoustic analysis.