Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies
Martin Wöllmer, Florian Eyben, Stephan A. Reiter, Björn Wolfgang Schuller, Cate Cox, Ellen Douglas‐Cowie, Roddy Cowie · 2008
Class based emotion recognition from speech, as performed in most works up to now, entails many restrictions for practical applications. Human emotion is a continuum and an automatic emotion recognition system must be able to recognise it as such. We present a novel approach for continuous emotion recognition based on Long Short-Term Memory Recurrent Neural Networks which include modelling of long-range dependencies between observations and thus outperform techniques like Support-Vector Regression. Transferring the innovative concept of additionally modelling emotional history to the classification of discrete levels for the emotional dimensions “valence ” and “activation ” we also apply Conditional Random Fields which prevail over the commonly used Support-Vector Machines. Experiments conducted on data that was recorded while humans interacted with a Sensitive Artificial Listener prove that for activation the derived classifiers perform as well as human annotators.