Classical and novel discriminant features for affect recognition from speech

Abstract

This paper investigates the performance and relevance of a set of acoustic features for the task of automatic recognition of affect from speech using machine learning techniques. Eighty seven novel and classical features related to loudness, intonation, and voice quality, are examined. Using feature selection, the results yield a performance level of 49.4% recognition rate (compared to a human performance rate of 60.4% and a chance level of 20%), while the relevance results show that the more exploratory and novel subset of these features outrank the more classical features in the recognition task.