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Bilingual acoustic feature selection for emotion estimation using a 3D continuous model

Emotions are complex human phenomena. Numberless researchers have attempted a variety of approaches to model these phenomena and to find the optimal set of emotion descriptors. In this paper, we search for the most appropriate acoustic features to estimate the emotional content in speech based on a continuous multi-dimensional model of emotions. We analyze a set of 6,920 features using the feature selection method known as Linear Forward Selection. We studied the importance of the features dividing them into groups and working with two databases, one in English and one in German to analyze the multi-lingual importance of features and to know if these features are important regardless of the language.