Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods\ndo not consider the relationship between emotions.Thus, in this study, a speech emotion recognition system based on the fuzzy\ncognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is\nproposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between\nemotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large\nnumber of concepts,whereas a typicalFCMcan handle only relatively simple networks (maps). Different acoustic features, including\nfundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three\nexperiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison\nbetween the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases.\nResults of the feature combination experiments show that the recognition rates of the combination features are 10%ââ?¬â??20% better\nthan those of single features. The proposed FCM learning algorithm generates 5%ââ?¬â??20% performance improvement compared with\ntraditional classification networks.
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