A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

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.

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

“…Li et al (2016) proposed a group decision making model for integrating heterogeneous information. A new Fuzzy Cognitive Map learning Algorithm for Speech emotion recognition was developed by Zhang et al (2017). Ceska et al (2019) developed Shepherding Hordes of Markov Chains.…”

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“…In [102] a new FCM learning framework (e-FCM) proposed used for recognizing speech emotion. In this approach, the pleasure-arousal-dominance emotion scale is employed to measure the casual relations among emotions where the structure of the network is determined via certain mathematical derivations.…”

Classification and Feature Transformation with Fuzzy Cognitive Maps

“…During last 30 years numerous applications of FCMs have been proposed in various domains. Their (not exhaustive) list includes: support of medical decisions [6], identifying gene regulatory network from gene expression data [7], recognition of emotions in speech [8], process control [9], ecosystem modeling [10], analysis of development of economic systems and introduction of new technologies [11], academic units development [12], prediction of time series [4], traffic prediction [13] project risk modeling [14], reliability engineering [15] and security risk assessment for IT systems [16,17].…”