Likability of human voices: A feature analysis and a neural network regression approach to automatic likability estimation

Abstract

Recently, the automatic analysis of likability of a voice has become popular. This work follows up on our original work in this field and provides an in-depth discussion of the matter and an analysis of the acoustic parameters. We investigate the automatic analysis of voice likability in a continuous label space with neural networks as regressors and discuss the relevance of acoustic features. We provide results on the Speaker Likability Database for comparison with previous work and a subset of the TIMIT database for validation.