Research On Artificial Bee Colony Algorithm And Its Application In Speech Recognition

Swarm intelligent optimization algorithm is inspired by the living habits of gregarious biology, and currently it has become a hotspot to solve complex optimization problems. Artificial Bee Colony (ABC) algorithm is a new type of swarm intelligent optimization algorithm, which is inspired by the behavior of bees finding honey. In the optimization process, ABC algorithm can well balance the exploitation and detection processes of food source. It can escape from the local optima to a certain extent, and find global optimal solution with the larger probability. Thus more and more researchers pay attention to it.Speech recognition technology is a key technology to exchange information between people and computers. Speech recognition model is an important speech recognition module, and therefore how to make better recognition model is a key subject in the field of speech recognition. In this thesis, two improved ABC algorithms are proposed on the base of the performance analysis and theoretical study of artificial bee colony algorithm, and then the applications in speech recognition model are discussed too. The results of speech recognition show that the ABC algorithm and improved ABC algorithms have good optimization performance. The main works and innovative results of this thesis are listed as follows:(1) The background and research status of ABC algorithm are described. The generation mechanism of ABC algorithm is studied in depth. The commonalities and characteristics of ABC algorithm with other intelligent optimization algorithms and the time complexity of algorithm are analyzed. The optimized performances of ABC algorithm are tested by optimizing standard functions.(2) The theoretical research is performed on ABC algorithm. The strict mathematical definitions are given for the state and state-space of the artificial bee and artificial bee colony and one-step transition probability of artificial bee colony. The state transition process of artificial bee colony is proved to be a finite homogeneous Markov chain process. According to the convergence criteria of stochastic optimization, global convergence of artificial bee colony algorithm is analyzed. The martingale definition is used to analyze the search process of ABC algorithm where the changing process of fitness is a submartingale stochastic process. Then the everywhere strong convergence of the algorithm is proved by submartingale convergence theorem.(3) According to the analysis result of ABC algorithm which is prone to premature and slowly convergence speed, this paper proposed two improved method from two points of view which are Chaos time Variant Artificial Bee Colony (CTABC) and Ranking Disruptive Selection Artificial Bee Colony Algorithm (RDABC).①CTABC:Firstly chaotic map is used to initialize the population for increasing population ergodicity. Secondly the Time Variant parameters factor is joined in search equation of onlooker bees which change the search space and speed up the search efficiency according to the change of iterative times in the search process. Finally in order to make the algorithm better escape from local optima, chaotic search is applied in the scout bees search stage.②RDABC:When onlooker bees select food source, the fitness value is ranked based on the rank fitness selection strategy. Then the selected probability is calculated by rank ordinals using disruptive selection method. This selection aigorithm maintains the diversity of the population and improves search precision.(4)In the Discrete Hidden Markov Model (DHMM) isolated word speech recognition system, the problem of low speech recognition is caused by the quantization error of vector quantization. The paper proposes the two modified DHMM speech recognition algorithms which use ABC algorithm and CTABCalgorithm to cluster speech feature vector and generate the optimal codebook in the DHMMspeech recognition system. In ABC and CTABC algorithms, each food source indicates a codebook. The optimal codebook is obtained by using bee evolution ways to iterative initial codebook. The optimal codebook enters the DHMM to be trained and recognized. The experimental re sults show that the modified DHMM speech recognition algorithm has higher recognition ratio and better robustness.(5) In order to overcome the shortages that BP algorithm does not well find the global optimum and is easy to fall into local minimum values, a novel hybrid learning algorithm for fuzzy neural network(FNN) parameters is proposed, hybrid learning algorithm is that apply ABC and RDABC clustering algorithm to determine the centers of the membership function and use the sample points around centers determine the widths of the membership function and use pseudo-inverse method determine the weight between normalization layer and output layer. The FNN trained by hybrid learning algorithm is used in speech recognition system which improves the ability of generalization and self-learning of FNN and is able to determine the fuzzy rule numbers according to the vocabulary to be recognized. The experimental results show that the FNN optimized by hybrid learning for speech recognition system have faster convergence, higher recognition ratio and better robustness than FNN trained by PSO algorithm and BP algorithm.(6) Because of the support vector machine (SVM) kernel functions and parameters have a large impact on classification performance of SVM,the research of parameter optimization method of SVM is very necessary.In order to solve the problem of falling into local optimal solution of all the common SVM parameters selection methods, a novel SVM parameters optimization method based on ABC algorithm is proposed. In this method, food-source position is the penalty factor and kernel parameter, and fitness value is classification accuracy function of SVM. The process of ABC searching optimal food source is the process of SVM parameters selection. Compared speech recognition results with PSO algorithm, the proposed algorithm is a good parameters optimization method of SVM, which not only can overcome the local optimal solution problem and increase speech recognition ratio but also enhance robustness and generalization ability of SVM.