Study Of Speech Recognition Based On Fuzzy Neural Network Optimized By PSO

In recent years, speech recognition is an artificial intelligence technology that is developping rapidly, which has broad application prospects. It is a hot research spot which has been widely applied in the Internet, communications, military, national defense and human-computer interaction.It is the key that nonlinear of the speech signal and semantic ambiguity in the speech recognition research. Therefore, fuzzy neural network(FNN) combined nonlinear and self-learning of artificial neural network with fuzzy inference and fuzzy partition of fuzzy system is applied in the speech recognition quickly. Particle swarm optimization(PSO) algorithm is a global optimization algorithm that has good performance, while it is a promising neural network training algorithm. In this paper, the in-depth research is done to the speech recognition based on fuzzy neural network optimized by PSO. The main achievements are as follows:1,Fuzzy neural network trained by particle swarm optimization algorithm is proposed in order to overcome shortage of traditional BP algorithm which replies on initial conditions, has the longer training time, and is easy to be trapped into the local extremum. The paper introduces the inertia factor changed adaptively and dynamically in order to balance the glabal and local search ability. Taking into account the dimension of the speech feature in speech recognition is large, and the structure of fuzzy neural network is complicated, it is inappropriate to optimize all parameters by PSO. Therefore, PSO algorithm is used to cluster the center of fuzzy layer, while the width and the weight is respectively got by the distance measure algorithm and the pseudo inverse algorithm. The trained network is use to the speech recognition. The experiment results show that the proposed algorithm has good performance, which is better than the network trained by BP algorithm in the recognition rate and the convergence speed.2,Point at the problem of premature convergence of the basic PSO algorithm, it introduces quantum-behaved particle swarm optimization (QPSO). And two optimization algorithms is tested using four typical test functions. The results show that QPSO is easier to find the global optimal value than the basic PSO. Therefore, fuzzy neural network optimized by QPSO is proposed.3,Aimed to the shortage of the old system that has strong dependence on the environment and has complicated operation, an improved speech recognition system is proposed. It is trained to be a template library by speech feature in two SNRs environments combined low SNR with noiseless. The trained network is appropriate to recognize in any SNR environment, which improves the adaptability to environment, and simplifies the process of speech recognition. Finally, fuzzy neural network optimized by QPSO is used to the improved speech recognition. The experiment shows that the improved system has better optimization performance, compared with the result of the old system which does the experiment in the same condition.