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Most previous studies using the dimensional approach mainly focused on the direct relationship between acoustic features and emotion dimensions (valence, activation, and dominance). However, the acoustic features that correlate to valence dimension are very few and very weak. As a result, the valence dimension has been particularly difficult to predict. The purpose of this research is to construct a speech emotion recognition system that has the ability to precisely estimate values of emotion dimensions especially valence. This paper proposes a three-layer model to improve the estimating values of emotion dimensions from acoustic features. The proposed model consists of three layers: emotion dimensions in the top layer, semantic primitives in the middle layer, and acoustic features in the bottom layer. First, a top-down acoustic feature selection method based on this model was conducted to select the most relevant acoustic features for each emotion dimension. Then, a button-up method was used to estimate values of emotion dimensions from acoustic features by firstly using fuzzy inference system (FIS) to estimate the degree of each semantic primitive from acoustic features, then using another FIS to estimate values of emotion dimensions from the estimated degrees of semantic primitives. The experimental results reveal that the constructed emotion recognition system based on the proposed three-layer model outperforms the conventional system

Improving speech emotion dimensions estimation using a three-layer model of human perception