Speaker- And corpus-independent methods for affect classification in computational paralinguistics

The analysis of spoken emotions is of increasing interest in human computer interaction, in order to drive the machine communication into a humane manner. It has manifold applications ranging from intelligent tutoring systems to affect sensitive robots, from smart call centers to patient telemonitoring. In general the study of computational paralinguistics, which covers the analysis of speaker states and traits, faces with real life challenges of inter-speaker and intercorpus variability. In this paper, a brief summary of the progress and future directions of my PhD study titled Adap- Tive Mixture Models for Speech Emotion Recognition that targets these challenges are given. An automatic mixture model selection method for Mixture of Factor Analyzers is proposed for modeling high dimensional data. To provide the mentioned statistical method a compact set of potent features, novel feature selection methods based on Canonical Correlation Analysis are introduced.