Canonical Correlation Analysis and Local Fisher Discriminant Analysis based Multi-View Acoustic Feature Reduction for Physical Load Prediction

Abstract

In this study we present our system for INTERSPEECH 2014 Computational Paralinguistics Challenge (ComParE 2014), Physical Load Sub-challenge (PLS). Our contribution is twofold. First, we propose using Low Level Descriptor (LLD) information as hints, so as to partition the feature space into meaningful subsets called views. We also show the virtue of commonly employed feature projections, such as Canonical Correlation Analysis (CCA) and Local Fisher Discriminant Analysis (LFDA) as ranking feature selectors. Results indicate the superiority of multi-view feature reduction approach to its single-view counterpart. Moreover, the discriminative projection matrices are observed to provide valuable information for feature selection, which generalize better than the projection itself. In our preliminary experiments we reached 75.35% Unweighted Average Recall (UAR) on PLS test set, using CCA based multi-view feature selection. Index Terms: ComParE 2014, acoustic feature selection, Canonical Correlation Analysis, Local Discriminant Analysis, Physical Load