Discovering dimensions of perceived vocal expression in semi-structured, unscripted oral history accounts

Abstract

What do people hear in expressive, unprompted speech? And how can their descriptions be transformed into a representative set of dimensions of vocal expression? This paper presents a methodology for collecting user description of vocal expression, transforms the user descriptions into a set of measurable expressive dimensions, and derives a representative feature set and baseline classifiers across these dimensions. The resulting classifiers recognized the top 13 dimensions over an oral history corpus, with a maximum unweighted recall score of 80.5%.