Speech big data cluster modeling based on joint neural network and Spark-SVM with evolutionary intelligence
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Speech big data cluster modeling based on joint neural network and Spark‑SVM with evolutionary intelligence Haowen Chen1 Received: 17 March 2020 / Revised: 1 June 2020 / Accepted: 27 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Big data · Data clustering · Speech information · Neural network · Spark-SVM
1 Introduction With the advent of the cloud computing era, the big data has attracted much attention. The development of a large number of the real-time application systems is facing big data problems such as large scale, many types, and fast changes. Complex event processing is one of the key technologies of flow computing platform. Under the event-driven architecture, the complex events are processed in the way of event flow processing. By extracting the sequence of the events that conform to a specific pattern and processing it in real time, it can meet the demand of high throughput and low latency in mass data processing. Based on the literature review, the current frameworks of the cloud big data services can be summarized into the following core aspects. (1) Enterprises provide their customers with “cloud” infrastructure, which consists of multiple servers while providing services such * Haowen Chen [email protected] 1
School of Journalism and Communication, Hunan Mass Media Vocational Technical College, Changsha, Hunan Province, China
as storage resources and virtualized servers required for the entire industry is achieved by integrating memory, I/O devices, storage and computing capabilities into a virtual resource pool that can greatly reduce the user’s overhead on hardware, which is the advantage of IaaS. (2) Think of the software as a service
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