Soft linear discriminant analysis (SLDA) for pattern recognition with ambiguous reference labels: Application to social signal processing

Abstract

While most pattern recognition approaches are designed and trained with clearly defined reference labels, there are a few new applications working with ambiguous ones. Since the linear discriminant analysis (LDA) is one of the most utilized methods in pattern recognition to reduce the dimensionality of feature vectors, typically increasing the robustness of the features, we propose in this work a modification of the LDA in order to be able to handle ambiguous reference labels in a soft-decision way. In the field of social signal processing (here: emotion recognition) we demonstrate that using a soft accuracy measure evaluating the classifier's confidence output by means of a soft-labeled emotional speech database really provides a degree of similarity to (naturally ambiguous) human votes. The adaptation of our classifier to such soft accuracy measure takes place by a retraining w.r.t. the human vote distribution. Applying this soft accuracy measure to emotion recognition with ambiguous reference labels both retraining the classifier and using the new soft LDA method leads to around 22% relative increase of accuracy.