Practical considerations on the use of preference learning for ranking emotional speech

Abstract

A speech emotion retrieval system aims to detect a subset of data with specific expressive content. Preference learning represents an appealing framework to rank speech samples in terms of continuous attributes such as arousal and valence. The training of ranking classifiers usually requires pairwise samples where one is preferred over the other according to a specific criterion. For emotional databases, these relative labels are not available and are very difficult to collect. As an alternative, they can be derived from existing absolute emotional labels. For continuous attributes, we can create relative rankings by forming pairs with high and low values of a specific attribute which are separated by a predefined margin. This approach raises questions about efficient approaches for building such a training set, which is important to improve the performance of the emotional retrieval system. This paper analyzes practical considerations in training ranking classifiers including optimum number of pairs used during training, and the margin used to define the relative labels. We compare the preference learning approach to binary classifier and regression models. The experimental results on a spontaneous emotional database indicate that a rank-based classifier with fine-tuned parameters outperforms the other two approaches in both arousal and valence dimensions.