This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable More
This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable structures are a reinforcement learning method. Having no information about optimized operation, such models try to find an answer to a problem. Converging speed in such algorithms in solving different problems and their route to the answer is so that they produce a proper condition if the answer is obtained. However, despite all tricks to prevent the algorithm involvement with local optimal, the algorithms do not perform well for problems with a lot of spread local optimal points and give no good answer. In this paper, the fusion of stochastic learning automata algorithms has been used to solve given problems and provide a centralized control mechanism. Looking at the results, is found that the recommended algorithm for partitioning constraints and finding optimization problems are suitable in terms of time and speed, and given a large number of samples, yield a learning rate of 97.92%. In addition, the test results clearly indicate increased accuracy and significant efficiency of recommended systems compared with single model systems based on different methods of learning automata.
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Speech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification meth More
Speech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification method using multi-constraints partitioning approach on emotional speech signals. To classify the rate of speech emotion signals, the features vectors are extracted using Mel frequency Cepstrum coefficient (MFCC) and auto correlation function coefficient (ACFC) and a combination of these two models. This study found the way that features’ number and fusion method can impress in the rate of emotional speech recognition. The proposed model has been compared with MLP model of recognition. Results revealed that the proposed algorithm has a powerful capability to identify and explore human emotion.
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