Automatic multi-lingual arousal detection from voice applied to real product testing applications

A method is presented which applies Long Short-Term Memory Recurrent Neural Networks on real market-research voice recordings in order to automatically predict emotional arousal from speech. While most previous work has dealt with evaluations of algorithms within the same speech corpus, the novelty of this paper lies in an extensive evaluation across corpora and languages. The approach is evaluated on seven large data sets collected in real tests of TV commercials and new product concepts across four languages. We observe excellent performance within and between the different corpora when compared against the gold standard of arousal ratings by human annotators. Even in the cross-language validation the models show good performance which almost reaches human rater agreement.