Classical and novel discriminant features for affect recognition from speech
Raul Castro Fernandez, Rosalind W. Picard · 2005
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. In the active research area of recognition of affect from speech it is of particular interest to obtain acoustic features that provide results closer to those of human recognition abilities. While many now “classic” features have been proposed in the literature, their performance has still fallen short of human recognition, suggesting the need to continue a search for novel features and methods. This paper briefly highlights results from an extensive investigation developing new features, and comparing them side-by-side with classical ones using machine learning techniques. (See [1] for many details omitted in this paper.) Algorithms and features associated with modeling loudness, intonation, and voice quality are highlighted in § 2, 3 and 4 respectively, and results of the experiments in §5 with some concluding remarks in § 6.