Abstract
Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps). Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%–20% better than those of single features. The proposed FCM learning algorithm generates 5%–20% performance improvement compared with traditional classification networks.
Highlights
Speech emotion recognition is a method of recognizing emotions from human speech signals
This paper proposes a new learning method, which includes the use of the three-dimensional PAD model and certain mathematical derivations, to overcome the aforementioned shortcomings
The new learning algorithm for e-fuzzy cognitive map (FCM) is established for speech emotion recognition
Summary
Speech emotion recognition is a method of recognizing emotions from human speech signals. Different studies on speech emotion recognition have been conducted, including studies on searching for available acoustic features. Only a limited number of classifiers for speech emotion recognition have been reported Most classifiers, such as support vector machines (SVMs) [12], knearest neighbor (KNN) classifiers [13], and neural networks (NNs) [14], have a wide range of applications, including emotion recognition. Their recognition rates are low, when two or more similar emotions should be simultaneously identified
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