Speech big data cluster modeling based on joint neural network and Spark-SVM with evolutionary intelligence

Emerging development of artificial intelligence application scenarios have caused the bursting requirement for the speech analysis tools, the efficient speech data processing model is urgently needed. This paper analyzes the speech big data cluster modeling based on joint neural network and Spark-SVM. As one of the important functions of graph data mining and analysis applications, graph clustering mainly implements classification operations on each node in the graph model based on clusters, and at the same time increases the association of the object entities, we then use this feature to design the model. In practical applications, subject to the size of the training data set, the system recognition rate does not show a steady upward trend with the increase of Gaussian mixture. In the case of limited speech data, the model parameters that can be then reliably estimated are limited. Because the input vector in the neural network structure is mostly abstract data, the BN layer in the hidden layer must be located in the back of the network structure, which makes the hierarchical performance results more profound. Hence, the joint neural network model is designed. The Spark structure is implemented to improve the systematic efficiency. We simulate the model and compare with the state-of-the-art models.